Composited Sequence

As part of my cell visualisation project, I have been working on a second set of data, which also represents the growth and development of cancer cells. This dataset however, focuses on the stage of which the individual cells are in, with the intention of identifying the most effective time for treatment to take place.

With thanks to one of the mathematicians working in this area, I was provided with a large quantity of data (around 33 million lines of numerical information) which had been generated, and could be used to drive my 3D visualisation. I created a MEL script which could 'read' each section of this data and then generate/animate the appropriate objects in 3D space.

After completing this stage, I then setup the scenes for rendering - adding lighting, placing cameras, adjusting render settings and creating render layers. Using the VERL render farm, I generated approximately 33,000 renders which could be composited together to create a completed 3000 frame sequence (depicting 600 hours of cell growth and development).

Using After Effects, I combined all of the image sequences and created several individual compositions which would be layered to create the final output. Because almost all of the work had been completed in Maya, there was very little that had to be done in After Effects, except combine the appropriate layers. Rather than generate a QuickTime MOV, I rendered a master image sequence (after speaking to one of my classmates) which could then be used to create suitable video files as and when needed.

Because the source of my output comes from unpublished mathematical models, I am unable to post any of the video online (it would be unfair to the ongoing research still being carried out). Instead, I have opted to include an image of a single frame, which can be seen below;

Over the next few weeks, I will be meeting with the mathematician that I created this for, as there are several upcoming maths conferences and events where my work could be used to illustrate the research they are carrying out.

Compositing Goodness...

Following on from my most recent post (here), I continued to develop the scene I had been working on as a 2D image in Photoshop (using 3D renders from Maya).

I then added additional layers/objects to the Maya scene file and organised the appropriate render layers. Image sequences were rendered, and imported into After Effects, where a 10-second sequence was constructed - using the same 'style' as the prototype image.

The completed test sequence can be seen below;

Moving forwards, I would like to continue this visual development, perhaps with the addition of camera movement and depth-of-field techniques.

Having Fun!

In terms of technical development, my knowledge of 3D software, scripting skills and problem solving abilities have surpassed those that I need to be able to complete the projects I am currently working on.

This has given me the time and opportunity to focus on visual experimentation, bringing a bit more fun back into my work, and making it more interesting than searching through pages of MEL commands!

I have been experimenting with using the skills gained in the Going Live module, to enhance the output and presentation of my previous cell visualisation work - using my skills as a digital artist.

Starting with a previous data-set, I adapted one of my scripts to create locators instead of spheres. I then created a simple particle system and used a modified version of a script provided by the external examiner to 'attach' the particles to the locators. This meant that I could use Maya's own 'metaball' system - not strictly metaballs, as it is a particle render type called "Blobby Surfaces", but it gives a similar effect. The image below shows a beauty render of the blobby surfaces;

Once this model had been created, I started to experiment with shaders. After reading some articles in this months 3D Artist and 3D world magazines, I created an MIA mental ray shader, and added a mental ray fast skin shader (normally used for subsurface scattering) and adjusted the colours and attributes to create a suitable look.

I added lighting in the form of two area lights, which used the mental ray area light options to transform from squares into cylinders, 'wrapping' around my geometry. Decay was set to quadratic (to create more accurate lighting) and the intensity of the lights was increased significantly (around 4500 each).

The next stage was to incorporate dust motes floating around. This is something I could imagine in my head, but was not sure how to implement. I looked at adding this in post-production, but although this could be quicker, did not provide enough control (or use 3 dimensions). I created a new scene file and using a particle emitter, created a particle 'explosion' - the forces were then zeroed out, so that I had a static particle cloud. I added my own gravity and turbulence fields, and tweaked these until I had the movement that I liked.

Finally, I setup render layers to output the passes I wanted - a MIA shader pass, a second MIA with an outline style shader, and separate pass for dust motes. After rendering a single frame, I moved into Photoshop and started experimenting with compositing these passes together, to create the look I wanted. I also added some fake bokeh effects in the background, coupled with some randomly generated cloud textures. The final image can be seen below, looking entirely different to how it first started (above);

At this stage, I wanted to make sure that I could recreate this look with image sequences, so I started work in After Effects. Fortunately I was able to mirror this image in video form, and can swap in the rendered image sequences when finished. By working in AE, I realised that I would need to add a matte pass for the cell geometry. Below, a short video shows the breakdown of how this shot was constructed, and although static, shows how a final video could look;

I have thoroughly enjoyed this experimentation, and I have created something I am really happy with - something very different to the first attempts (which can be seen in an earlier post here). Although I don't yet see this as a finished piece, I can already see ideas developing, and it is good to try new techniques and methods of presenting the same mathematical data... more importantly it is good to get back to being an artist, something that I did not realise I missed until now!

Advanced Production (Summary)

Despite returning to Semester 2 later than expected (due to illness), I have made excellent progress, with major breakthroughs in overcoming many of the challenges my project was facing.

My programme of study takes a more visualisation based route, and I am working with the University mathematics division to visualise numerical data (gained from their mathematical models) which represents the growth and development of cancer cells (or solid tumour growth).

Working with numerical data has created it's own set of challenges, as Maya cannot read or interpret this in it's off-the-shelf form. As a result of this problem, I have developed skills in using Maya's own scripting language, MEL (Maya Embedded Language) and also a second scripting language (used more widely) called Python. Although this has been a difficult and time-consuming process (several months of learning, which is still ongoing) I have been able to harness this new understanding and create custom tools which can be used to read the mathematical data and create appropriate 3D geometry.

Throughout learning each scripting language, I referred primarily to online sources, and the 'official' documentation provided. I also used the Digital Tutors service, which was both informative and relevant, helping me overcome the hurdle of knowing where to start, and what to focus on.

Through creating my own custom tools, I was able to work with the numerical data-sets provided. Starting with simple tests, I was (eventually) able to construct fully animated scenes containing simple spheres, representing the individual cancer cells, and although time-consuming to process, these results opened the way for more advanced development of the data and it's visual attributes.

I spent some time experimenting with Maya's render layers, and compositing passes using Nuke. This was as a response to feedback given last semester - not everything has to be done in 3D, as it can often be quicker and easier to complete some work in 3D, but fine-tune the details in post-production. This helped me develop possible ideas for the look of cells, when considering texture on a microscopic basis, and I also experimented with 'faking' depth-of-field techniques to enhance this.

Although creating spheres was great, I wanted to push my skills further. I spent time working with RealFlow, Cinema 4D and Houdini - three great 3D packages. With the exception of RealFlow, these were entirely new to me, and although Cinema 4D was fairly straight-forward, Houdini had a very steep learning curve. Using my Python scripting skills, I created a large-scale metaball system, equivalent to the spheres created in Maya - this was a challenging task, forcing me to solve several problems, whilst trying to script in a language mostly new to myself. The result of this technical experimentation allowed me to create an interesting almost-organic 3D structure, which moved and behaved as one surface, instead of 1068 individual cells.

After completing these Python related tasks, I returned to Maya to work with additional data-sets, still showing the growth and development of cancer cells, but in a different format. This data would require not only animated movement of cells, but changing colours, and eventually, complex density 'clouds'.

The difficulty at this stage in my work, was the size of the data-sets... one of which contains over 30 million lines of information (a text file weighing in at around 800mb). Despite breaking this into smaller chunks of data, it was still computationally intense, and difficult to work with (often crashing high speed computers). I spent time streamlining and optimising my scripts, and the way they handled the data - as an example, my oxygen density script was originally taking around 350 seconds per frame to process, whereas now it takes around 90 seconds, about a quarter of the time. As I have learned more about MEL, and gained a better understanding of the language, I have been constantly improving and refining my scripts, to ensure the best efficiency when working with large data sets.

I am currently working with new additional data-sets, which again have constantly required me to think and problem solve... now that my scripts are optimised, I am confident that my scripting abilities are suited to a wide range of data types, across two very different scripting languages. This allows me to tackle upcoming problems with new knowledge, based on the experiences I have had over the last ~3 months, giving me a definite headstart.

In summary, my practice-based learning has developed the following;

• Scripting - both MEL and Python, for simple and complex tasks (including 3D visualisation)
• Improved software knowledge - Maya, RealFlow, Nuke
• New software knowledge - Cinema 4D, Houdini
• Advanced 3D skills development - particles/dynamics, data input/output, script optimisation, rendering
• Experience in 2D compositing with 3D-based image sequences

Due to the sensitive nature of the mathematical data I am working with (and as the source data is effectively the result of unpublished research) I am unable to post a large amount of my visual work online. However, this sensitive material will be made privately available during course presentations, and to module assessors as necessary.

Visible Progress!

Over the last couple of weeks, my role in the Going Live project increased significantly, and then stopped completely. All of the animated shots had lighting added, and then I added the render layers/passes and started feeding completed shots through the render farm (which was considerably faster than I expected it to be). Sound effects and music were then added by the sound team, creating our finished advert.

It took a long time to get there, and there were problems along the way, but I learned a lot (particularly about rendering and compositing) and I am glad we all got there in the end! Next week, we are due to meet with the company in their London studio and present our finished project - hopefully the feedback will be good!

As for my cell visualisation work, this has been making good progress since my role in Going Live has lessened.

The first data-set I was working with, which represented cells and fibres in 2D space, now has fully working scripts, which are streamlined to work efficiently (or to actually work at all!). I am currently awaiting feedback on the video outcome of this work, so that I can decide where to take this next.

The other data-sets (involving cells, blood vessels, and oxygen density maps) have made even better progress. Again, after optimising my MEL scripts, the amount of data (several million lines of information) has become manageable, although time-consuming to process. I am currently part of the way through 'translating' this data into Maya's 3D environment.

An example render from the cells file can be seen below. This example frame is approximately two thirds of the way through the cell data, and incorporates some 'noise' on the cell surfaces to break up the uniformity (an idea suggested by the mathematician who provided the data);

As for the oxygen density, I decided to continue using a single polygonal plane for this, with grid points in the data having a matching vertex on the 3D geometry. The data then lifts/lowers each grid point/vertex between 0 and 1, where 1 is the most dense area of the oxygen 'clouds'.

The 'look' of these clouds is then controlled using one of two shaders.

Shader 1 ("Clouds") is coloured white, and uses a vertically-aligned ramp shader for it's transparency value, where 0 is fully transparent and 1 is fully visible. This means that as points on the vertex grid are changed in the Y-axis, their transparency is also changed (as they are moved higher, they become more visible).

Shader 2 ("Bands") expands upon this idea, and uses a second ramp for the colour (from blue to red, low to high). The transparency ramp is also 'sliced' into bands which are evenly spaced vertically - this means that only the narrow bands are visible, giving us slices of colour (where the colour is defined by where the slice falls on the colour ramp, rather than a fixed colour). This gives a result similar to the high/low pressure bands which weather presenters often use, but with colour added.

I have included a video below, which better explains these shaders - the white 'cloud' is shader 1, and the coloured 'bands' are shader 2;

Although this video shows a top-down view of the scene, it is important to remember that these effects are generated in 3D - moving forwards, I could include moving camera or changing points of view to highlight particular events.

Also, the oxygen density visuals are considered another 'layer' which I can add to the cells and blood vessels, creating a more complete final output.

I am not sure as to how this final output will look at the moment, as I am still developing the visual elements of each of the data-sets, but progress is good and things are at least working now...

Needs more juice...

Similar to my last blog entry, my efforts are still divided between two main projects;

The Going Live project has been making excellent progress. The team and I have continued improving the visual outcome of the 3D elements. With a texture in place, lighting and rendering have started to progress rapidly. Animated shots are being lit, rendered and composited, so that final testing can take place.

Although I have taken on the role of CG Supervisor, I have been involved in other areas, and responsible for others. I was involved in tracking the camera footage and creating a 3D pre-visualisation of the sequence. I have monitored the 3D pipeline continuously, and offered advice and created fixes for problems - this includes working with others in modeling, texturing, rigging, animation, and lighting. I was responsible for creating a dynamic cloth system which would allow for easier animation, and I have managed the rendering process (involving creating render layers and using the render farm). Finally, I get to pass rendered scenes to the compositing team, who can work their magic... and before I know it, we will have finished the advert!

Returning to my cell visualisation work, I have reached a temporary plateau. I have developed my pipeline/workflow and have implemented new and improved scripts to manage the new data that I have received from my colleagues in the Mathematics division.

However, the vast quantity of data has been as proving difficult to process efficiently. After some development, I have speeded up the process, but this still takes a considerable amount of time to complete. Fortunately, I discussed this with my project supervisor/programme leader, and agreed the use of high-specification computers in the University, where I can process data quickly, making use of round-the-clock facilities. This week, I will begin using these facilities, to start working with this new data that I have received.

It is important that I begin this early, as the sooner the numerical data has been translated into 3D space, the sooner I can begin developing the visual qualities of the mathematical models. Completing this visualisation process becomes even more important, as the mathematical models will be presented as part of a conference in June - giving me a deadline to work towards...

More Data

Over the last couple of weeks, I have had to divide my attention between multiple projects.

The 'Going Live' project has ramped up into production, with modeling and rigging now complete. Animation has started, and texturing is currently underway. My role as CG Supervisor has been demanding, as all of these CG elements have been happening in quick succession. On top of this role, I was also responsible for implementing a customised nCloth dynamics system for our 'character'. This was created alongside the rigging process, to ensure that these components would work together happily, and after resolving a few technical problems, the system is now working nicely. My next task was to create and organise the appropriate render layers in Maya, ready for rendering and then compositing to take place (hopefully late this week or early next). Although I had worked with render layers before, this project requires more variants than I am used to working with, so has taken a bit of time to configure and setup properly. Despite all this work taking up more time than initially expected, the project has made good progress, and continues to do so.

After my meeting with mathematics last week (and several more since), my cell visualisation workload has increased also. I have received new data from both students, and I am currently in the process of writing scripts that will translate these into 3D scenes inside Maya.

The first new data set contains fibres (to be added to cells), which are based on xyz locations and xy rotations. I had not scripted rotation values yet, so this was a good opportunity to expand my knowledge of MEL. I am currently awaiting the full data-set for this part of the visualisation, so will continue to work on this moving forwards.

The other data set adds oxygen density to a scene containing cancer cells and blood vessels. This file contains over 30 million lines of information, and weighs in at around 800mb - making it rather difficult to work with. I have tried different approaches in visualising this data efficiently, such as adjusting transparency on cubes based on the density value or scaling particle clouds. Unfortunately, there are about 10,200 points per frame, so these methods take far too long to calculate. I am currently testing a new method, which creates a single polygonal plane, with the required number of vertices. The script then runs through each vertice, and moves it in the y-axis based on the density value (between 0 and 1). A ramp shader then adjusts the transparency of the plane based on the height (where 0 density is fully transparent). This creates white, cloudy patches where oxygen density is high. Although this still takes a long time to process, it is considerably faster than the other methods.

Most of this work is still on-going, and has 'arrived' at the same time, making it difficult to balance. Fortunately, I have been able to allow extra time in working on these projects, so hopefully the worst of it is over now...

On a more exciting note, three of my videos were used at an event in Dundee on Saturday 12th March. The videos are 3D visualisations of mathematical models which are being used to predict cancer growth and development, and were developed in collabroation with a PhD student in the University's mathematics division. They were shown at an event called "Sensational Women in Science" as part of the Women in Science Festival 2011.

Also, some of the other data I am currently working with will be presented at a large conference later this year (in June), so I have a deadline which I can work towards.

Biomedical Visualisation

With a new year, comes new ideas and refreshed inspiration!

Over the last couple of weeks, I have been developing my MEL scripting abilities - something necessary if I want to work with importing numerical data into Maya. Although daunting at first, things have gradually started to make sense, showing the logical development of the stages involved in trying to achieve my goal.

Last week, I had a major breakthrough in using MEL, and was able to create a script which would read one of the mathematical data-sets. The script works on a line-by-line basis, reading comma separated values and placing those into individual variables, which are then used to create objects and keyframe animation. Upon running the script, all actions are automated, and require no input from the user - this simplifies the process, and speeds up creation of a Maya scene greatly. It also ensures that no mistakes are made, as long as the script is correct and data is formatted consistently.

An example of the type of data being used can be seen below;

The purpose of this script is to convert raw numerical data into something visual, built in 3 dimensions. Currently, the script uses simple polygonal spheres to represent cells, although in the future this could be changed to use particles, or something different entirely. An example render of the scripts process can be seen below - the results are dramatically different to looking at thousands of line of numbers;

At this stage, I was confident in my MEL writing abilities, so created 2 more (similar) scripts which would work with the other mathematician's dataset. This data is entirely different however, as the cells are placed in 2 dimensions, and instead of changing size/radius, they change colour based on a numerical value. This posed it's own problems, as I would need to have a new shader for every cell, if I wanted them to change colour individually. The scripts for this data work, but are still in early stages - no renders have been produced as of yet.

Beyond being able to get data into Maya, I was then free to experiment with the visual aspects of representing the data. I tested several techniques which would be useful later on, including using render layers (for alpha channels and depth passes), adjusting camera settings in Maya (to create depth of field) and compositing render layers using both Nuke and After Effects (using short image sequences). Although familiar with compositing techniques in Photoshop, I wanted to familiarise myself with these methods when working with videos and image sequences.

As the final part of my experimentation, I started working with the first data-set in 3D, and added a camera and some basic lighting. I rendered 3 passes - 'beauty', alpha and depth - and composited each of these layers using Photoshop. After some experimentation, I realised that I was happy with the result, and would be confident in replicating the visual style using finished image sequences. The completed composite can be seen below;

Although I am only 2 weeks into this semester, I have made tremendous breakthroughs in my own programme of study, particularly with using MEL to import numerical data. I hope to continue this progress throughout the semester!

Moving forwards, I would also like to continue developing my technical skills and abilities - this will ensure that I have the best opportunities to create high quality work, with no restrictions on the software I can use to achieve this. I hope to continue working with RealFlow, and hope to have some visual examples soon. I also plan on developing skills in using Houdini, another 3D package, with a more technical focus.

Reflection On "The Story So Far..."

In a recent post, titled "The Story So Far..." I talked about the work I have been doing, and the progress I have made with the various sources of learning. This post aims to expand on this, with some added reflection on my programme of study, and the 'journey' so far.

Initially, I commenced my studies at DJCAD with the intention of focusing on 3D animation - specifically gaining a better understanding of the principles of animation, and how to apply them in being a better animator. This interest came from the work I had undertaken as part of my Honours studies, where I created a short animation depicting a story from Greek mythology - "Theseus And The Minotaur" (this was an individual project).

However, after taking advantage of the opportunity to attend presentations by a wide range of people, I gained an insight into using 3D computer graphics as a visualisation tool - this was thanks to an insight into the work being done by John McGhee and Chris Rowland, although I was more interested in the idea of biomedical visualisation.

This change in direction forced me to change my programme of study to something more appropriate, and as discussed in "The Story So Far..." I have since spent time developing the necessary skills in 3D computer graphics.

These skills have been relevant to my programme of study, as I am currently involved in two projects which make use of this new technical undertstanding. The first project is in collaboration with the University mathematics division, and involves developing more visual methods of visualising mathematical data (more specifically, cell visualisation). The second project is a visual-effects based project, in collaboration with another MSc student. As part of this project, I am responsible for 3D modelling, and creating a dynamics simulation.

Although each of these projects have had difficulties, good progress has been made and the experience has been invaluable. If the projects did not have any problems, I would not have learned nearly as much as I have, and I would not be as prepared for future projects as I am now.

Now that I have a stronger understanding of 3D computer graphics and their use in visualisation, I can focus on the application of these skills, and concentrate on completing these projects.

Moving forwards, I would like to continue developing my 3D abilities, and gear this specifically towards using RealFlow for advanced dynamics simulation (allowing me more flexibility in the type of visuals I can create) and learning MEL scripting within Maya (potentially giving me the option to automatically generate visuals from huge amounts of mathematical data).

Considering the drastic change of direction in my programme of study, I am glad I started developing skills in visualisation. Moving forwards, I am excited by the range of projects out there, and look forward to developing my own visual style.

Visualisation Techniques : Cells (Continued)

Continuing my experimentation with how my cells could look (first post here), here are three more examples which all use a spherical soft-body as a starting point. These examples also show a change in colour - something relevant to the mathematical data I am working with.

The first example combines previous render techniques, and uses a Cloud shader applied to the particles. Unfortuantely this gave the cell a glowing appearance, and had no distinct shape or outline.

The second example instead uses particles which are invisible, using a Blinn shader applied to the soft-body surface directly (the particles are used solely to drive the animation of the cell). A 2D fractal was used as a bump-map, ensuring that the surface was not too smooth and plain.

The third and final example builds on the second, using the same Blinn shader, also applied to the surface. The difference is that the surface material is created using a Layered Shader, which uses the original Blinn (made almost transparent) and a second copy which uses a Ramp Shader to adjust the transparency based on the object's facing ratio (making the shader less transparent towards the object edges). Although there are two shaders layered here, it gives a more interesting look - a transparent looking cell with a clearly defined outline.

Inspiration 1 : Cell Visualisation

In collaboration with the University mathematics division, I am working on cell visualisation - starting off with mathematically generated data, I am importing this into Maya and defining the aesthetics of the scene, making the data more accessible and visually exciting.

Alongside my own work, I have found some examples of cell visualisation that I am particularly interested in. The first of these is a clip called "The Inner Life of the Cell", created in 2006 for Harvard biology students, by a company called BioVisions. Although this animated sequence looks dated, compared to today's standards, the content (and it's importance) are still just as relevant today. A tremendous amount of effort was put into to this project, and those working on it were constantly aware of the relationship between the quality of the visuals and the accuracy of the data. One criticism I would make, is that the scenes are often very 'busy' and feature lots of moving items and lots of different colours. Although this means there is more to look at, it can also make the shots somewhat confusing, as there is no clear focus. "The Inner Life of the Cell" can be seen below;

BioVisions have also continued working on molecular animations, with their latest video titled "Powering the Cell: Mitochondria" (a clip can be viewed here). This video is a significant update to the other one above, primarily thanks to the improvements in technology over the last four years. Although the concept is the same, the video has been output in high-defination, and this is definately a noticeable improvement. The visual style has also 'quietened' down somewhat, and is much more pleasing to the eye, as can be seen in the image below;

Moving away from this type of visualisation, I am particularly fond of "Nature by Numbers" created by Cristobal Vila. This is an expertly created piece of work, and focuses on how nature is driven by mathematics (at it's core). The content is of excellent quality, and there are segments where it appears that some sort of dynamics system has been used to drive the animation - something I am currently developing skills in. The overall look of the video has a very polished feel, something I would certainly hope to achieve by the end of my MSc programme! The video can be seen, in all it's high-definition glory, below;

After looking at other examples of work out there, it is clear to see that there is a great deal of importance placed on both the quality/accuracy of the data, and the appeal of the visuals. Trying to find this balance however, can pose difficult, and it is important for an artist to find an individual style which suits them. As mentioned in a previous post, this reinforces the importance of experimention - practice makes perfect.

Visualisation Techniques : Cells

Experimentation is often the key to success, and computer graphics are no exception...

Recently, I have been manually building a 3D scene from some mathematical data (due to problems using MEL to import this automatically), so I needed to start work on how the data could be represented.

This first data-set features cells which multiply over time, and also change colour (which represents their type, or stage). Previously I had already tested changing particle colours (early results of this can be found here) so it was time to experiment with how these spherical 'cells' might look.

Working separately to the data setup earlier, I started off creating a polygonal sphere, and using a surface emitter to create the particles. I started by key-framing the emission rate, but then decided to output the required amount of spheres and set the initial state (so that we did not see the creation of the particles). I created a simply three-point lighting setup, and animated a camera moving through a 90-degree arc - the scene was now ready for aesthetic testing.

This first example shows the particles, rendered using the Blobby Surface render type. The Radius and Threshold were then key-framed and oscillated, to create a moving, pulsing surface. Ideally I wanted each particle to pulse individually, but I had difficulties in doing this. Using a Blobby Surface created a simple effect, with required very little computational time on render - something which might outweigh the 'awkward' pulse effect when hundreds of cells are required (and can pulse at different intervals to each other).

This second example builds on the first, although uses the Cloud render type. This was combined with a Lambert surface, and used the same animated Radius and Threshold. The cell was animated to rotate on the XYZ-axis, giving more variance visually. I preferred the effect created here, as it seemed more random, but there was not enough definition in the shadows or highlights, forcing the cell to appear flatter than it actually is. Also, several 'holes' appeared in the surface, which was an unwanted effect.

This third and final example is a development of the second, and uses a Ramp shader instead of a Lambert shader combined with a Particle Cloud node. The Ramp shader used the 'glass' preset, and was recoloured to be more neutral. I found that this video looked the best, and gave almost a glass-like look to the cell, with visible shadows and strong specular highlights. When the cell turns green, the glass outer-casing becomes more apparent, something which contributed to the overall style of the cell.

Although great progress has been made here, and I particularly like the third example, it took considerably longer to render. Also, the cell still 'pulsed' in an unnatural fashion - something I would like to correct moving forwards.

Although more work still needs to be done, I happy with the results so far, and it is always good to see progress being made.

Snow Is Falling...

After realising Gnomons 'Dynamics' series wasn't going to be a good source of learning for me, I decided to give Digital Tutors a try. After a quick look through their material, I started on their 'Introduction to Dynamics in Maya' lessons (more information here).

Looking at the individual tutorials within this lesson, there was bound to be some overlap. However, I felt that it would provide a good opportunity to consolidate the learning I already have, and fill in any gaps in my knowledge (I find Digital Tutors to be extremely thorough in explaining features).

After completing the first 5-6 lessons, I decided to experiment with some colour techniques which will prove useful for my project with the University mathematics division. Part of this project will feature cells which change type, and each type is associated with a different colour - I needed to find a way to animate an object between colours effectively. More importantly, I needed to find a way to control the colour changes in particles, as these are more likely to be used moving forwards.

The first example shows some simple 'particle rain/snow' which has keyframed colour changes, and works very well. Although the particle effects are not what I'm looking for, I was testing colour here, and this has worked as hoped. Since I already have Lambert shaders setup with the colour changes, I also needed to find a way to use these, as particles use Particle Cloud shaders. Fortunately I can simply plug the coloured Lambert into the colour input of the Particle Cloud shader, providing an additional level of control.

The second example shows something a bit more fun. After my experimentation in the first video, I realised I could make some decent-looking snow. After modeling a basic landscape, with a hill and some simple trees, I created a snow particle effect, with a small amount of randomness applied to it. After creating this short clip, it really made me wish there was snow outside!

Target Acquired

This week I have had meetings with students from the University's Mathematics division, with the intention of beginning a collaborative visualisation project.

After introductions and a brief discussion of the topic, I will undertake work which will involve developing more visual methods of visualising mathematical cell data - that is, data generated from mathematical models designed to project movement or growth of cancer cells.

Working with students outside of Duncan of Jordanstone will pose it's own problems though - artists and mathematicians speak very different 'languages' and this will be a tremendous learning opportunity, giving me experience of working with new people, in new subject areas (cell biology and mathematics).

This also opens up a huge variety of possibilities in terms of creativity, and will allow me to continue my work as a 3D artist, developing skills in dynamics and fluids, amongst other things. I will also have the opportunity to work with real-world data, and engage in research alongside another department within the University - a positive outcome in many ways for those involved.

With a goal in sight, and areas of development to focus on, this really is 'target acquired'!

And so it begins...

After speaking with my programme leader/supervisor, I have decided to focus on visualisation and developing my skills and abilities towards this. This will include looking at using things such as Maya's dynamics systems, particles and scripting capabilities - all new to me.

I have chosen to spend Reading Week looking at these particular areas, and have picked two main sources to help lead my practice.

The first of these is a book that came specifically recommended - 'In Silico: 3D Animation and Simulation of Cell Biology using Maya and MEL', written by Jason Sharpe, Charles J Lumsden and Nicholas Woolridge, and published by Morgan Kaufmann Publishers. This book will be used to augment my existing Maya knowledge, and develop skills which are directly related to visualising cell biology.

The second source(s) I have chosen, is a set of DVD tutorials from The Gnomon Workshop called Dynamics. There are a great number of these, but I will be trying to start at the beginning and build a strong foundation. I currently have discs 1-3 from the University library to get me started.

Hopefully these sources will get me going in the right direction, as next week I hope to have a meeting with the University's Mathematics department, and possibly start working on a visualisation project with them.

In the meantime, keep your eyes posted for examples of what I've been doing this week... once I've done it of course!