Temporary Coherence 3D Animation
- 1. Temporary Coherent 3D Animation Akshat Singh B.Tech IV year
- 2. Table of Content » Defination » Method » Steps Involved » Acquisition System » Data Acquisition » Unified Skeletal Animation Reconstruction » Temporally Coherent 3d Animation Reconstruction » SIFT features » Merging of three cameras » Bone-length Variation » Evaluation
- 3. Defination Method for capturing human motion over 360 degrees by the fusion of multi-view RGB-D video data from Kinect sensors using feature point sampling
- 4. Method » Capture real-world objects and technique ranging from shape matching to deformation toward coherent animation » Use RGB data,3D reconstruction for deapth reconstruction » Background segmentation dealing with RGB color space
- 6. Acquisition System » frame of input RGB and Depth images » separately resampled in a 3D point cloud » point clouds are merged in a unified global coordinate system
- 7. » RGB frames from three cameras » Frontal and profile faces are detected in two cameras » depth data with the overlaid skeleton from Kinect » unified skeleton from the two cameras towards which the actor's face is oriented Data Acquisition
- 8. Merging of three cameras » unified two point clouds black and red » corresponding two skeletons after the extrinsic calibration » Unified skeleton reconstructed
- 9. Unified Skeletal Animation Reconstruction(Still) » unified two point clouds black and red » corresponding two skeletons after the extrinsic calibration » Unified skeleton reconstructed
- 10. Unified Skeletal Animation Reconstruction Zoomed-out point cloud with feature points shown in blue and green. Red point at time-step t is to be matched, and green points are the five nearest feature points. Shows the zoomed-in point cloud at t. Motion vectors are calculated with respect to the 5 nearest feature points. These motion vectors are used to calculate the matching point at t+1 ,not centered on any point because the matching is resolution independent
- 11. Temporally Coherent 3d Animation Reconstruction Estimating Optical Feature Points Estimating Geometrical Feature Points Mapping Unified skeleton reconstruct from method Alignment using Motion Vectors
- 12. SIFT features using a simple Euclidean distance measure D » Matching of optical feature points between two RGB images using SIFT. » SIFT feature has a location q(t) = (u, v, t) » Optical feature points L(t) » mapping between L(t) and L(t + 1)
- 15. Evaluation » Comparison against direct RGB-D SLAM » Comparison against feature-based RGB-D SLAM » Evaluation of the residual configuration » Depth vs inverse depth in the geometric reprojection error » Computational time » Failure modes » Qualitative results
- 16. Place your screenshot here Code Link » https://github.com/ alejocb/rgbdtam » https://www.youtube.co m/watch?v=sc- hqtJtHD4
- 17. THANK you! Any questions? You can find me at » Linkedin/akshat7497 » akshat7497@gmail.com