Image-generation diffusion models can draw arbitrary visual-patterns. What if we finetune Stable Diffusion to 🖌️ draw joint actions 🦾 on RGB observations? Introducing 𝗚𝗘𝗡𝗜𝗠𝗔 paper, videos, code, ckpts: genima-robot.github.io/ 🧵Thread⬇️

Manifold Diffusion Fields present Manifold Diffusion Fields (MDF), an approach to learn generative models of continuous functions defined over Riemannian manifolds. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifold via the eigen-functions of the Laplace-Beltrami Operator. MDF represents functions using an explicit parametrization formed by a set of multiple input-output pairs. Our approach allows to sample continuous functions on manifolds and is invariant with respect to rigid and isometric transformations of the manifold. Empirical results on several datasets and manifolds show that MDF can capture distributions of such functions with better diversity and fidelity than previous approaches paper page: huggingface.co/papers/2305.1…

Ever wondered why deep learning is always done on array data?🤔 Happy to announce our work: From data to functa: Your data point is a function and you can treat it like one 📝arxiv.org/abs/2201.12204 w/ @emidup @arkitus @DaniloJRezende @danrsm, to appear in ICML22