Iterative Optimization Easy Matting Model the unknown region as a Markov Random Field. Introduce a local refinement technique to manipulate the continuous energy field in selected local regions. Energy-driven scheme can be extended to video matting. Specifically, we propose an energy minimization framework for interactive image matting. As shown in this example, we start from a few strokes, and iteratively estimate the matte until it converges. Notice the fact that no explicit trimap is required. Beginning with the known region, we model the unknown region as a Markov Random Field (MRF) and formulate its energy as the combination of one data term and one smoothness term. Second, we introduce a novel local refinement technique to manipulate the continuous energy field in selected local regions. The modified local regions can be seamlessly integrated into the final result. Lastly, our approach can be directly extended to video matting by considering an additional temporal smoothness term, with which the spatio-temporal smoothness is faithfully preserved. Iterative Optimization Initial Input Final Matte

Results Knockout 2 Poisson Bayesian Input image Trimap Strokes Now I will show some results of our system. We can use bayesian, Knockout 2 and Poisson matting to extract matte from this image based on the same trimap. However, they yield more visual artifacts than our approach, even with more user inputs. Starting from a few strokes, BP matting didn’t give us the right matte, however, our system can generate a good matte. Strokes BP Matting Global Easy Matting

Conservative Voxelization Conservative correctness: all voxels intersecting the input model are recognized. Efficient and robust implementation in the GPU. No preprocessing required. Previous approach: generate a single voxel for each pixel by using the depth in the pixel center Our approach: generate multiple voxels for each pixel by computing the depth range in the pixel

Application to Collision Detection Efficient (in real-time) Support deformable models Conservative correctness: colliding voxels refer to potentially colliding regions non-colliding voxels refer to regions with no intersection Collision detection between the buddha model (210k triangles) and the morphing hand model (5k triangles) is accomplished in 114 ms (~8.8 fps)

Data-driven Tree Animation Synthesis Adapt the motion synthesis algorithm in Human animation to tree animation. Advantages: realistic & efficient Contributions: A practical sampling algorithm leading to a rich and reusable motion database; Improved algorithm for motion graph construction; Efficient algorithm for motion synthesis which has a fast response to user interaction.

Dynamic Forest Scene Demo