CVNov 29, 2023

Talking Head(?) Anime from a Single Image 4: Improved Model and Its Distillation

arXiv:2311.17409v24 citationsh-index: 6Has Code
Originality Incremental advance
AI Analysis

This work reduces the cost of creating avatars and games for anime character applications, though it is incremental as it builds on an existing open-source project.

The paper tackles the problem of generating real-time controllable anime character models from a single image by improving the THA3 system, achieving a distilled model under 2 MB that generates 512x512 frames at over 30 FPS on consumer GPUs while maintaining image quality close to the full system.

We study the problem of creating a character model that can be controlled in real time from a single image of an anime character. A solution to this problem would greatly reduce the cost of creating avatars, computer games, and other interactive applications. Talking Head Anime 3 (THA3) is an open source project that attempts to directly address the problem. It takes as input (1) an image of an anime character's upper body and (2) a 45-dimensional pose vector and outputs a new image of the same character taking the specified pose. The range of possible movements is expressive enough for personal avatars and certain types of game characters. However, the system is too slow to generate animations in real time on common PCs, and its image quality can be improved. In this paper, we improve THA3 in two ways. First, we propose new architectures for constituent networks that rotate the character's head and body based on U-Nets with attention that are widely used in modern generative models. The new architectures consistently yield better image quality than the THA3 baseline. Nevertheless, they also make the whole system much slower: it takes up to 150 milliseconds to generate a frame. Second, we propose a technique to distill the system into a small network (less than 2 MB) that can generate 512x512 animation frames in real time (under 30 FPS) using consumer gaming GPUs while keeping the image quality close to that of the full system. This improvement makes the whole system practical for real-time applications.

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