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3D Crowd Generation with Stable-Diffusion and Blender. A thread exploring current simplest approaches, limitations and possible improvements. #stablediffusion #crowdgen #b3d

157,025 次观看 • 3 年前 •via X (Twitter)

10 条评论

Alex Martinelli 的头像
Alex Martinelli3 年前

Any generative-model could work, but stable-diffusion has an awesome ecosystem, and can leverage ControlNet to generate full-body controllable poses. Then is all about good prompting and quality fix (e.g. hd +face-restoration).

Alex Martinelli 的头像
Alex Martinelli3 年前

Similarly, PIFuHD ( doesn't always generate the best results, but is easy and fast. Potential better alternative are already there, for example ECON ( more powerful but slower.

Alex Martinelli 的头像
Alex Martinelli3 年前

Biggest limitation is texture. We can project the original stable-diffusion image, but is missing sides and back. Projects exist already for this, for example The idea is to use again generative-models to generate (or complete) a texture given a 3D object

Alex Martinelli 的头像
Alex Martinelli3 年前

Example: a crowd of knights #stablediffusion #b3d

Alex Martinelli 的头像
Alex Martinelli3 年前

Example: "sci-fi" crowd #stablediffusion #b3d

Alex Martinelli 的头像
Alex Martinelli3 年前

I just published an article describing step-by-step the process and tools to achieve the results showcase in the video. Feel free to drop comments, feedback and requests. #stablediffusion #crowdgen #b3d

Duranovsky 的头像
Duranovsky3 年前

If you generate them in a Tpose, icon them and then rig/animate them in Mixamo, you will end up with a crowd that moves. And you can program a sequence of looped motions to make them fight, cheer or just chill out

Alex Martinelli 的头像
Alex Martinelli3 年前

I'm exactly working on that too. Simple process, but can't access Mixamo programmatically, so you gotta rig it manually, which doesn't scale.

ceejay achu 的头像
ceejay achu3 年前

detailed tutorial!!! please!!

fabio boehl 的头像
fabio boehl3 年前

Wow

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DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

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161,530 次观看 • 2 年前