CADiscussMay19 < Imager

---+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
   * [[#PaperOverview][Paper Overview]]
   * [[#PaperDiscussion][Paper Discussion]]

-----------------------------------------------------------------

#PaperOverview
---++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 ([[ftp://ftp.cis.upenn.edu/pub/badler/public_html/gmod/0528a.pdf][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 (???)

--------------------

#PaperDiscussion
---++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 Main.PhilippeBeaudoin)

-- Main.PhilippeBeaudoin - 19 May 2006
This topic: Imager > WebHome > CharacterAnimationGroup > CADiscussMay19
Topic revision: r1 - 2006-05-19 - PhilippeBeaudoin