Rate-Distortion Model Based Bit Allocation for 3-D Facial Compression Using Geometry Video

Junhui Hou, Lap-Pui Chau, Ying He, Minqi Zhang, Nadia Magnenat-Thalmann

Abstract: With the extensive applications of 3-D multimedia technology, 3-D content compression has been an important issue, which ensures its smooth transmission on the network with constrained bandwidth. In this letter, we propose a new compression framework for dynamic 3-D facial expressions. Taking advantage of the near-isometric property of human facial expressions, we parameterize the dynamic 3-D faces into an expression-invariant canonical domain, which naturally generates 2-D geometry videos and allows us to apply the well-studied video compression techniques. Due to the difference from natural videos, each dimension (i.e., X, Y and Z, respectively) of the geometry video is regarded as a video sequence and encoded separately. Meanwhile, a model-based joint bit allocation scheme is designed to allocate reasonable bitrate to each dimension by detailed analysis of rate-distortion model for geometry videos, to obtain optimal results under given target bitrate. Experimental results show that up to 25% improvement in terms of bitrate reduction can be achieved, compared to existing algorithms.