Video Generative Models as Geometry Learner
Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveraging off-the-shelf image diffusion models, they either (i) train task-specific geometry models (for depth and surface normal estimation) independently, losing the opportunity of exploring the intrinsic correlation of these geometric targets, or (ii) jointly fine-tune modified image diffusion backbones (e.g., altered self-attention), which typically demands substantial labeled data. To overcome these limitations in a principled fashion, we repurpo
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Video Generative Models as Geometry Learner” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “Video Generative Models as Geometry Learner” ≈ “Developer-Y/cs-video-courses””
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Video Generative Models as Geometry Learner” ≈ “steven2358/awesome-generative-ai””
- LinkedLinked via arxiv author · 85%Haosen Yang →
“Video Generative Models as Geometry Learner”
- LinkedLinked via arxiv author · 85%Jifei Song →
“Video Generative Models as Geometry Learner”
- LinkedLinked via arxiv author · 85%Zhensong Zhang →
“Video Generative Models as Geometry Learner”
- LinkedLinked via arxiv author · 85%Xiatian Zhu →
“Video Generative Models as Geometry Learner”
- LinkedLinked via arxiv author · 85%Jiankang Deng →
“Video Generative Models as Geometry Learner”