So now we have to compute the geodesic distance from every bone to every vertex. To solve this problem, I did something similar to Volumetric Heat Diffusion, which you can read about on wolfire’s blog: http://blog.wolfire.com/2009/11/volumetric-heat-diffusion-skinning/. They also have a good example of the weight bleeding effect. Their idea is simple: take a discretized version of the model (in 3D, they need to use a voxel-grid, but in 2D, we can simply use our alpha-thresholded image), and then spreads the weight of the bone throughout the entire model as if it were a heating element. This is done by setting each voxel’s heat to the average of its neighbors iteratively until convergence. One it converges, they can lookup the “heat” of the voxel at the location of each vertex and use that to compute the weight of that bone. Once converged, the “heat” is proportional to the geodesic distance (which our friend Laplace can confirm for us), but convergence can take a lot of iterations, with each iteration requiring a loop over all of the pixels in the voxel-grid or image. As you can imagine, this can be quite slow, especially without access to the parallel processing power of the graphics card. So, I thought: why not just compute the actual geodesic distance in one iteration? While not embarrassingly parallel like the above method, Dijkstra's algorithm does just that. Set the initial distance of all pixels in our image to infinity (or, if you use a 32-bit image like I did, an integer value which is sufficiently large). Then, set all of the pixels along the bone to 0, and add all of those pixels to a working queue. This can be done by treating the bone as a line, and using a line-rasterization algorithm to get all of the pixels in the image along that line. Now, until the working queue is empty, dequeue the next pixel, and for every neighboring pixel that is within our outline (using the same alpha-threshold as we did to generate the vectorized image) and is unvisited (meaning its distance is less than infinity), set that pixel’s distance to the current pixel’s distance plus one, and add it to the queue. Visually, this is quite simple, the distance along the bone is zero, the distance of pixels adjacent to the bone is one, and so on.
|The generated normalized weights for the body bone (red), the upper leg bone (green), and the lower leg bone (blue). Note how the higher smoothness has a smoother transition of weights at the joints.|
For those of you who know Dijkstra's algorithm, my algorithm is not quite the same, it’s an optimization assuming that the distance from one pixel to any neighboring pixel is the same (which it is as we always add one to the distance). Also, for those of you who really like to analyze algorithms, you may notice that this means that the distance from one pixel to a diagonal pixel is 2, not v2. This means that we aren’t really getting the shortest distance within the outline, but the shortest manhattan distance within the outline. This can be fixed by following Dijkstra's algorithm without my optimization and including the diagonals as neighbors with a weight of v2, but this requires additional computation and updates of pixels, and does not make a significant difference in the assigned weights of the bones.
|The result of the above bone weights with the bones bent into a sitting position. The higher smoothness gives a smooth bend, but looks too smooth at the hip joint. The lower smoothness only bends a small part of the joint.|
So, now that we have the distances computed, how do we actually assign the vertex weights? Obviously, the larger the distance, the less the weight, but how much less? The answer to that is: it depends! If the weight fades a lot with distance, then you get a hard, angular joint that is good for elbows. If the weight fades slowly with distance, then you get a soft, smooth joint that is good for backs and hair. I found that an exponential function tends to work well: e^(-distance/smoothness). This function is always one for zero distance, and drops off quickly with a low smoothness, and slowly with a high smoothness. Let the artists decide what smoothness is best. Don’t forget to normalize the vertex weights so that they add to one! Also, you do not need to store all of the bone weights per vertex - usually storing the four highest weighted bones is enough. Then, to transform the vertices, compute the transformation matrices for each bone, and then the transformed vertex position is the sum of the vertex position transformed by each bone’s matrix weighted by the bone’s weight. Obviously, if a bone’s weight is one, then the vertex is transformed by just that bone, and at the joints, it will smoothly interpolate between the transformations of the nearby bones.
|A sample showing the how layers will work in VIDE. The arm does not bend with the body in this example as it is in a different layer, and can rotate independently.|
We now have a working system that can deform images based on bones. VIDE is done now right? Unfortunately, making this tool usable will require layers, animation tracks, and all sorts of UI stuff. But the point is that we can now animate and deform images! Who cares if anyone can use the program or not, right? All joking aside, look forward to more updates on VIDE, as well as updates on some of the game projects I’m currently working on.