Detailed Human Avatars from Monocular Video
This work addresses the challenge of generating realistic digital humans for applications like virtual reality or gaming, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of creating high-detail human avatars from monocular video, achieving results that are superior to state-of-the-art methods in identity preservation, detail, realism, and user preference.
We present a novel method for high detail-preserving human avatar creation from monocular video. A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides. Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution texture. Our paper contributes facial landmark and shading-based human body shape refinement, a semantic texture prior, and a novel texture stitching strategy, resulting in the most sophisticated-looking human avatars obtained from a single video to date. Numerous results show the robustness and versatility of our method. A user study illustrates its superiority over the state-of-the-art in terms of identity preservation, level of detail, realism, and overall user preference.