Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Pan, Xingang"'
Drag-based editing has become popular in 2D content creation, driven by the capabilities of image generative models. However, extending this technique to 3D remains a challenge. Existing 3D drag-based editing methods, whether employing explicit spati
Externí odkaz:
http://arxiv.org/abs/2410.16272
Autor:
Xing, Zhening, Fox, Gereon, Zeng, Yanhong, Pan, Xingang, Elgharib, Mohamed, Theobalt, Christian, Chen, Kai
Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio, thanks to their temporally uni-directional attention mechanism, which models correlations between the current token and previous tokens. However
Externí odkaz:
http://arxiv.org/abs/2407.08701
Autor:
Wolski, Krzysztof, Djeacoumar, Adarsh, Javanmardi, Alireza, Seidel, Hans-Peter, Theobalt, Christian, Cordonnier, Guillaume, Myszkowski, Karol, Drettakis, George, Pan, Xingang, Leimkühler, Thomas
The real world exhibits rich structure and detail across many scales of observation. It is difficult, however, to capture and represent a broad spectrum of scales using ordinary images. We devise a novel paradigm for learning a representation that ca
Externí odkaz:
http://arxiv.org/abs/2406.08924
The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional challenges in the time dimension, image editing has witnessed the developme
Externí odkaz:
http://arxiv.org/abs/2405.16537
Video generation primarily aims to model authentic and customized motion across frames, making understanding and controlling the motion a crucial topic. Most diffusion-based studies on video motion focus on motion customization with training-based pa
Externí odkaz:
http://arxiv.org/abs/2405.14864
Despite the emergence of successful NeRF inpainting methods built upon explicit RGB and depth 2D inpainting supervisions, these methods are inherently constrained by the capabilities of their underlying 2D inpainters. This is due to two key reasons:
Externí odkaz:
http://arxiv.org/abs/2405.02859
Autor:
Lan, Yushi, Hong, Fangzhou, Yang, Shuai, Zhou, Shangchen, Meng, Xuyi, Dai, Bo, Pan, Xingang, Loy, Chen Change
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper i
Externí odkaz:
http://arxiv.org/abs/2403.12019
Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward models that learn to infer the 3D model of an object without optimizati
Externí odkaz:
http://arxiv.org/abs/2403.12409
Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However, a notable limitation of diffusion models, in comparison to GANs, is their difficulty in smoothly interpolating between two image sample
Externí odkaz:
http://arxiv.org/abs/2312.07409
Based on powerful text-to-image diffusion models, text-to-3D generation has made significant progress in generating compelling geometry and appearance. However, existing methods still struggle to recover high-fidelity object materials, either only co
Externí odkaz:
http://arxiv.org/abs/2308.09278