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Can we apply diffusion directly on MLP weights? Yes!!! We show generalization to new 3D shapes and 4D mesh animations: "HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion" #ICCV2023! Project: Video:

Matthias Niessner

48,486 subscribers

86,046 просмотров • 3 лет назад •via X (Twitter)

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Комментарии: 6

Фото профиля Matthias Niessner

Matthias Niessner3 лет назад

Quite interesting that neural fields can be directly used as representations for generative models. One open question is what MLP weight structure works best for generalization. Super cool work by @ErkocZiya in collaboration with @angelaqdai, @fangchangma, and Qi Shan.

Фото профиля Michael Black

Michael Black3 лет назад

@CasualEffects Cool idea.

Фото профиля Efstratios Gavves

Efstratios Gavves3 лет назад

We had a similar idea in our brainstorming sessions, curious to read in detail. Sounds excellent!!

Фото профиля Ruoshi Liu

Ruoshi Liu3 лет назад

Very cool work! How does it compare with MeshDiffusion whose geometric representation is more explicit?

Фото профиля 田中義弘 | taziku CEO / AI × Creative

田中義弘 | taziku CEO / AI × Creative3 лет назад

Potential technologies. Thanks for the info!

Фото профиля Tianyu He

Tianyu He3 лет назад

One question: why not generate triplane representation? since it more straightforward for diffusion models.

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119,949 просмотров • 3 месяцев назад

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140,992 просмотров • 2 лет назад

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🔥 Introducing MVLift: Generate realistic 3D motion without any 3D training data - just using 2D poses from monocular videos! Applicable to human motion, human-object interaction & animal motion. Joint work w/ Jiajun Wu & Karen 💡 How? We reformulate 3D motion estimation as generating consistent multi-view 2D pose sequences. Our framework uses 2D motion diffusion to progressively establish multi-view consistency, requiring only single-view 2D pose sequences for training. Project: Video with demonstration: Paper:

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15,880 просмотров • 1 год назад

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic paper page:

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic paper page:

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