GeoART: Geometry-Aware Refinement via Test-Time Scaling for Camera-Conditioned Video Diffusion

Key points

• Existing reme- dies often require costly geometry-aware fine-tuning or ad- ditional 3D supervision.1
• We introduce GeoART, a training- free self-refinement framework that improves camera con- trollability through geometry-aware test-time scaling.1
• At selected denoising steps, GeoART generates multiple can- didates within the Predict-and-Perturb cycle and eval- uates them using a geometry prior, without directly modify- ing the denoising trajectory.1
• Experiments demonstrate consistent improvements in camera controlla- bility, multi-view consistency, and visual quality over exist- ing sampling and self-refinement methods, without training or modifying the pretrained video diffusion model1

Sources (1)

• [1]GeoART: Geometry-Aware Refinement via Test-Time Scaling for Camera-Conditioned Video Diffusion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 01:40 PM

  Camera-conditioned video diffusion models often suffer from inaccurate camera control and geometric inconsisten- cies because camera constraints and estimated geometry are only weakly enforced during denoising.

  Existing reme- dies often require costly geometry-aware fine-tuning or ad- ditional 3D supervision.

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