虛擬視訊會議系統中利用虛擬代理人來代替使用者在同一虛擬場景中面對面交談，在過去研究的系統架構中整合了二維影像編碼及三維模型編碼的優點，在更低的頻寬要求下，讓虛擬代理人可以更逼真地呈現出和使用者相同的表情變化。
在本篇論文中，我們希望發展更適用於此系統二維影像編碼器之錯誤隱藏工具，在超低位元率傳輸下，當作加強層的二維影像資料發生遺失時，仍可利用當作基本層傳送的臉部動畫參數讓虛擬代理人達到應有的生動表情。
我們由輸入影像中分析出臉部動畫參數及有表情的人臉影像剪去無表情的人臉影像可得到兩者間的「紋理差異值」，再定義三維模型編碼所無法表現出人臉細緻表情變化的區域，將「紋理差異值」區分為幾個「部份紋理差異值」，並且根據臉部動畫參數的特性，每個「部份紋理差異值」都會跟隨著影響其區域的臉部動畫參數。
所以我們提出以類似向量量化的方法對影響各個區域的臉部動畫參數分類，選出以「部份紋理差異值」重建出的人臉影像，與原本的人臉影像之間得到最小的量化誤差，當作屬於此類別表情下的代表，因此我們可根據臉部動畫參數屬於的類別，選擇出其「部份紋理差異值」，藉此得到此時有表情之影像，來彌補可能遺失的部份。

Avatars are usually employed in virtual conferencing system as user’s agent for face-to-face talking with each other in a 3-D virtual space. In previous research, system architecture is established by integrating a 3-D model-based coder and a 2-D video coder under very low bit-rate video coding. It makes the avatars to have more realistic expression as users’ representatives to form the video realistic avatars.
In this thesis, we develop a new error concealment scheme for the 2-D video coder as the enhancement layer, which is expected to be more suitable in the virtual conferencing system. With the proposed scheme, we can alleviate the defect caused by the loss of the enhancement layer and reconstruct a vivid 3D virtual facial model while inefficient bandwidth is available.
First, we extract the facial animation parameters (FAP) and texture difference, which is the difference between expressive facial image and neutral facial image synthesized by 3-D model-based coding, from the input sequence. Then, we define the region of interest (ROI) by using facial animation tables (FAT) to separate the texture differences of the whole face into several partial texture differences in each ROI. Besides, each ROI are affected by specific FAP groups, that is the partial texture difference in ROI are also affected.
Thus, we propose a VQ-like algorithm to cluster the FAPs and its partial texture differences of each ROI according to the minimum quantization error, which is determined by the differences between synthetic facial image with partial updated by the partial texture difference and the real facial image. With the codevectors of each cluster, we can predict the partial texture difference by the nearest one to the received FAPs. Finally, a predicted texture map is generated by partial texture difference, and then we can conceal the enhancement layer by using these predicted texture map.

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