• A. Ahmed, A. Hilton, and F. Mokhtarian. Adaptive compression of human animation data. In Eurograhics - Short Paper, September 2002.
• Naka, T., Mochizuki, Y., Hijiri, T., Cornish, T., and Asahara, S. A compression/decompression method for streaming based humanoid animation. In Proc. of the 4th Symp. on VRML, 1999. http://doi.acm.org/10.1145/299246.299264
• T.K. Capin, E. Petajan, J. Ostermann. Very low bit rate coding of virtual human animation in MPEG-4, Proceedings of ICME 2000. http://ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=871554
• S. Chattopadhyay, S.M. Bhandarkar, K. Li. BAP sparsing: a novel approach to MPEG-4 body animation parameter compression, Proceedings Systems Communications, 2005. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1515510
• S. Chattopadhyay, S.M. Bhandarkar, K. Li. Compression by indexing: an improvement over MPEG-4 body animation parameter compression, Proceedings of SPIE, 2006. http://spiedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PSISDG00607100000160710K000001&idtype=cvips&gifs=yes

Discussion on May 19th 2006 reading group meeting

Paper presented: Arikan, Okan. Compression of Motion Capture Databases, to appear in Siggraph 2006 proceedings

• Project page (with paper and video): http://www.cs.utexas.edu/~okan/papers/s2006/compression.html
• Paper Overview
• Paper Discussion

Paper Overview

They present a method to compress a large database of skeletal motion data. Their method is separated in two parts:

1. Global motion compression
2. Specific precise compression of joints in contact with the environment (the feet in their case)

Global Motion Compression

• Each joint is associated to 3 virtual markers which are tracked through time. The marker positions are said to be more linear than the DOF angles. The DOF angles can easily be re-extracted from these markers, even after compression (using least-square fit).
• Separate the motion in 16-32 frames clips
• For each clip, fit a 3D Bezier curve through the moving markers
• Each clip is therefore a vector in a space of dimension d = 12 x 3 x number of joints .
• Reduce the dimension using Clustered PCA over the whole database
  • Spectral clustering using Nystrom Approximation (ref given)
  • Typically, 1 to 20 clusters
  • Randomly draw 10000 frames of the database before performing the CPCA
  • A parameter is used to decide how many dimensions are kept
• Quantize elements of the reduced vector to 16 bits.

Specific Joint Compression

• Ground reaction force is quite significant and applies over a veryshort time ==> High frequencies in the motion
• Sliding feet are a perceptually important artifact
• Consider the x,y,z coordinates of the virtual markers on the feet (or other contact joints) as separate 1D signals
• For each clip, apply DCT on these signals, then quantize, then entropy-encode (Huffman codes)
• During decompression, use IK (Tolani et al. 2006) to plant foot at reconstructed position.

Features and results

• To access an individual frame, one has to decompresse a 16-32 frame clip
• Compresses at 1 ms/frame, decompress at 1.2 ms/frame (7 times real-time)
• Random access any clip for decompression
• CPCA performed offline on a random 10000 frames of animation, clips can be processed independantly
• After CPCA, clips can be compressed independantly and incrementally. If statistical distribution changes, can perform CPCA again.
• Clip-to-clip transition can be discontinuous. Fix this by solving a sparse linear system over the clip, called Continuous Merge (???)

Paper Discussion

Here's what we think is missing in the paper :

• Progressive compression / Generating various animation LOD
• The technique doesn't take into account that the database is made of multiple sequences.
• Good results only if the database is large enough
• The frame-rates are not realistic for usage such as real-time games
• Incrementally compressing motion is efficient as long as the statistical properties do not change. When does that happen?
• Baseline comparison methods are probably too simple
• Decompression could exhibit cache issues since large chunk of data (PCA matrices) must be randomly accessed
• Compression is faster than decompression? Sounds weird...
• Justification for not using angular data is kind of weak

Here are some ideas that came out :

• Check how receptive field weighted regression would perform for temporal compression
• Use progressive compression technique for large mocap database exploration over a slow channel (see PhilippeBeaudoin)

-- PhilippeBeaudoin - 19 May 2006