Search Results for author:
Found 8 papers, 2 papers with code
StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos
This paper presents a novel framework for converting 2D videos to immersive stereoscopic 3D, addressing the growing demand for 3D content in immersive experience.
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames with precise camera pose control.
DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets.
MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement
Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge.
ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis
In this work, we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner.
HiFi-123: Towards High-fidelity One Image to 3D Content Generation
Our contributions are twofold: First, we propose a Reference-Guided Novel View Enhancement (RGNV) technique that significantly improves the fidelity of diffusion-based zero-shot novel view synthesis methods.
MPS-NeRF: Generalizable 3D Human Rendering from Multiview Images
For this task, we propose a simple yet effective method to train a generalizable NeRF with multiview images as conditional input.
