Search Results for author:
Found 6 papers, 1 papers with code
Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer
In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis.
PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns
Unlike prior arts constrained by specific input types, our method allows flexible specification of style (text or image) and texture (full garment, cropped sections, or texture patches) conditions.
Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data
The key is to first learn a part-wise 4D generative model from monocular images via adversarial learning, to synthesize multi-view images of diverse identities and full motions as training data; then leverage a transformer-based animatable triplane reconstructor to learn 4D head reconstruction using the synthetic data.
AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents
In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspectives.
Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors
In this paper, we introduce a simple and novel framework for one-shot audio-driven talking head generation.
Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head Synthesis
We present a novel one-shot talking head synthesis method that achieves disentangled and fine-grained control over lip motion, eye gaze&blink, head pose, and emotional expression.
