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Easy Matting

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. Iterative Optimization. Initial Input. Final Matte.

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Easy Matting

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  1. 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. Iterative Optimization Initial Input Final Matte

  2. Bayesian Poisson Knockout 2 Results Input image Trimap Global Easy Matting Strokes BP Matting

  3. Conservative Voxelization • Conservative correctness: all voxels intersecting the input model are recognized. • Efficient and robust implementation in the GPU. • Nopreprocessing required. Our approach: generate multiple voxels for each pixel by computing the depth range in the pixel Previous approach: generate a single voxel for each pixel by using the depth in the pixel center

  4. 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)

  5. 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.

  6. Dynamic Forest Scene Demo

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