CMU Lift4D: Single-view 4D reconstruction in-the-wild
rsasaki0109 · x · 2026-08-22
Carnegie Mellon University released Lift4D, a test-time optimization framework for reconstructing complete dynamic objects from monocular in-the-wild videos. Lift4D adapts a single-view 3D reconstruction model for temporally consistent predictions to initialize a deformable 3D Gaussian Splatting representation. It then refines this representation via occlusion-aware optimization to match the video, recovering visible details and completing unobserved regions using a view-conditioned diffusion prior.
More from Multimodal
- HyperFrames launches Reference Any Video and auto-applied Brand Kits for AI video — aftahi_ai · 2026-10-16
- Krea 2 + LoRA still beats Flux 3 Klein in pixel-art style test — Angaisb_ · 2026-10-16
- World Labs starts rolling out Atlas beta with pixel-perfect camera path control — theworldlabs · 2026-10-16
- Krea 3 image editing model lands on fal: high-quality edits in under 6 seconds — gorkem · 2026-10-16
- Tripo P2 vs Meshy 7.1: benchmarking today's best image-to-3D models for game assets — noahsolomon · 2026-10-16
- BytePlus-backed AI Film Award offers $1M grand prize for films 70% made with Seedance — DavidmComfort · 2026-10-16