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Found 9 papers, 3 papers with code
Exploring Text-to-Motion Generation with Human Preference
This paper presents an exploration of preference learning in text-to-motion generation.
Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress.
DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models
The generation of stylistic 3D facial animations driven by speech poses a significant challenge as it requires learning a many-to-many mapping between speech, style, and the corresponding natural facial motion.
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors.
O$^2$-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model
In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting model, to reconstruct complete surfaces for the hidden parts of objects.
Continuously Controllable Facial Expression Editing in Talking Face Videos
In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously.
Dynamic Neural Textures: Generating Talking-Face Videos with Continuously Controllable Expressions
In this paper, we propose a method to generate talking-face videos with continuously controllable expressions in real-time.
PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows
By considering noisy point clouds as a joint distribution of clean points and noise, the denoised results can be derived from disentangling the noise counterpart from latent point representation, and the mapping between Euclidean and latent spaces is modeled by normalizing flows.
Autoregressive Stylized Motion Synthesis With Generative Flow
Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics.
