Zobrazeno 1 - 10
of 165
pro vyhledávání: '"Durand, Fredo"'
Autor:
Yin, Tianwei, Gharbi, Michaël, Park, Taesung, Zhang, Richard, Shechtman, Eli, Durand, Fredo, Freeman, William T.
Recent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generators that match their teacher in distribution, without enforcing a one-
Externí odkaz:
http://arxiv.org/abs/2405.14867
Autor:
Sharma, Prafull, Jampani, Varun, Li, Yuanzhen, Jia, Xuhui, Lagun, Dmitry, Durand, Fredo, Freeman, William T., Matthews, Mark
We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism, employing a scalar value a
Externí odkaz:
http://arxiv.org/abs/2312.02970
Autor:
Yin, Tianwei, Gharbi, Michaël, Zhang, Richard, Shechtman, Eli, Durand, Fredo, Freeman, William T., Park, Taesung
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024
Diffusion models generate high-quality images but require dozens of forward passes. We introduce Distribution Matching Distillation (DMD), a procedure to transform a diffusion model into a one-step image generator with minimal impact on image quality
Externí odkaz:
http://arxiv.org/abs/2311.18828
Autor:
Tewari, Ayush, Yin, Tianwei, Cazenavette, George, Rezchikov, Semon, Tenenbaum, Joshua B., Durand, Frédo, Freeman, William T., Sitzmann, Vincent
Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenarios where training samples are readily available, which is not always th
Externí odkaz:
http://arxiv.org/abs/2306.11719
Autor:
Sharma, Prafull, Philip, Julien, Gharbi, Michaël, Freeman, William T., Durand, Fredo, Deschaintre, Valentin
Separating an image into meaningful underlying components is a crucial first step for both editing and understanding images. We present a method capable of selecting the regions of a photograph exhibiting the same material as an artist-chosen area. O
Externí odkaz:
http://arxiv.org/abs/2305.13291
Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers eff
Externí odkaz:
http://arxiv.org/abs/2305.10431
Autor:
Sivaraman, Vibhaalakshmi, Karimi, Pantea, Venkatapathy, Vedantha, Khani, Mehrdad, Fouladi, Sadjad, Alizadeh, Mohammad, Durand, Frédo, Sze, Vivienne
Publikováno v:
USENIX NSDI 2024
Video conferencing systems suffer from poor user experience when network conditions deteriorate because current video codecs simply cannot operate at extremely low bitrates. Recently, several neural alternatives have been proposed that reconstruct ta
Externí odkaz:
http://arxiv.org/abs/2209.10507
We study the problem of extracting biometric information of individuals by looking at shadows of objects cast on diffuse surfaces. We show that the biometric information leakage from shadows can be sufficient for reliable identity inference under rep
Externí odkaz:
http://arxiv.org/abs/2209.10077
Autor:
Sharma, Prafull, Tewari, Ayush, Du, Yilun, Zakharov, Sergey, Ambrus, Rares, Gaidon, Adrien, Freeman, William T., Durand, Fredo, Tenenbaum, Joshua B., Sitzmann, Vincent
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the bird's-eye-view
Externí odkaz:
http://arxiv.org/abs/2207.11232
Autor:
Bangaru, Sai Praveen, Gharbi, Michaël, Li, Tzu-Mao, Luan, Fujun, Sunkavalli, Kalyan, Hašan, Miloš, Bi, Sai, Xu, Zexiang, Bernstein, Gilbert, Durand, Frédo
We present a method to automatically compute correct gradients with respect to geometric scene parameters in neural SDF renderers. Recent physically-based differentiable rendering techniques for meshes have used edge-sampling to handle discontinuitie
Externí odkaz:
http://arxiv.org/abs/2206.05344