text-to-3D
text-to-3d-a848f6b0·2 events·first seen 28d agoAliases: text-to-3D, text-to-3D generation
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Point-E: A system for generating 3D point clouds from complex prompts
OpenAI introduced Point-E, a system for generating 3D point clouds directly from text prompts. The approach uses a two-stage pipeline: first generating a synthetic image from the prompt, then producing a 3D point cloud conditioned on that image. Point-E prioritizes speed over quality, generating coarse 3D shapes in seconds on a single GPU rather than requiring hours of compute like prior methods.
CARV: Compute-Aware Variance Reduction for Diffusion Teacher Gradient Estimation
CARV is a hierarchical Monte Carlo estimation framework that reduces gradient variance when using frozen pretrained diffusion models as teachers in downstream pipelines such as text-to-3D distillation and data attribution. The approach amortizes expensive upstream computation (rendering, simulation, encoding) over cheap diffusion-noise resamples, augmented by timestep importance sampling and stratified-inverse-CDF construction. In text-to-3D experiments, CARV delivers 2–3× effective compute multipliers; in single-step distillation, it cuts gradient variance by an order of magnitude but does not improve FID, revealing that MC variance is not the bottleneck in that regime.