BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:Asia/Tokyo X-LIC-LOCATION:Asia/Tokyo BEGIN:STANDARD TZOFFSETFROM:+0900 TZOFFSETTO:+0900 TZNAME:JST DTSTART:18871231T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250110T023312Z LOCATION:Hall B7 (1)\, B Block\, Level 7 DTSTART;TZID=Asia/Tokyo:20241205T164100 DTEND;TZID=Asia/Tokyo:20241205T165300 UID:siggraphasia_SIGGRAPH Asia 2024_sess138_papers_629@linklings.com SUMMARY:TextToon: Real-Time Text Toonify Head Avatar from Single Video DESCRIPTION:Technical Papers\n\nLuchuan Song and Lele Chen (Univeristy of Rochester), Celong Liu (Bytedance), Pinxin Liu (University of Rochester), and Chenliang Xu (Univeristy of Rochester)\n\nWe propose TextToon, a metho d to generate a drivable toonified avatar. Given a short monocular video s equence and a written instruction about the avatar style, our model can ge nerate a high-fidelity toonified avatar that can be driven in real-time by another video with arbitrary identities. Existing related works heavily r ely on multi-view modeling to recover geometry via texture embeddings, pre sented in a static manner, leading to control limitations. The multi-view video input also makes it difficult to deploy these models in real-world a pplications. To address these issues, we adopt a conditional embedding Tri -plane to learn realistic and stylized facial representations in a Gaussia n deformation field. Additionally, we expand the stylization capabilities of 3D Gaussian Splatting by introducing an adaptive pixel-translation neur al network and leveraging patch-aware contrastive learning to achieve high -quality images. To push our work into consumer applications, we develop a real-time system that can operate at 48 FPS on a GPU machine and 15-18 FP S on a mobile machine. Extensive experiments demonstrate the efficacy of o ur approach in generating textual avatars over existing methods in terms o f quality and real-time animation. Please refer to our project page for mo re details: https://songluchuan.github.io/TextToon/.\n\nRegistration Categ ory: Full Access, Full Access Supporter\n\nLanguage Format: English Langua ge\n\nSession Chair: Hongbo Fu (Hong Kong University of Science and Techno logy) URL:https://asia.siggraph.org/2024/program/?id=papers_629&sess=sess138 END:VEVENT END:VCALENDAR