VDPP is an RGB-free video depth post-processor that achieves over 43 FPS on Jetson Orin Nano by refining geometry at low resolution rather than reconstructing full scenes.
Depth any video with scalable synthetic data
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 6roles
background 1polarities
background 1representative citing papers
FFN performs efficient test-time training on multi-hour videos by forgetting the exiting frame, anticipating the next, and adapting only when a surprise metric exceeds a dynamic threshold.
One transformer, trained with random-sized temporal attention chunks, unifies offline, streaming, and long-video depth, normal, and point-map estimation and reports new best numbers on five public benchmarks.
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.
Fine-tuning text-to-video models on sparse low-quality synthetic data for physical camera controls outperforms fine-tuning on photorealistic data.
TPGDiff introduces hierarchical triple-prior guidance in a diffusion network, placing degradation priors throughout, structural priors in shallow layers, and semantic priors in deep layers for improved all-in-one image restoration.
citing papers explorer
-
VDPP: Video Depth Post-Processing for Speed and Scalability
VDPP is an RGB-free video depth post-processor that achieves over 43 FPS on Jetson Orin Nano by refining geometry at low resolution rather than reconstructing full scenes.
-
Forget, Anticipate and Adapt: Test Time Training for Long Videos
FFN performs efficient test-time training on multi-hour videos by forgetting the exiting frame, anticipating the next, and adapting only when a surprise metric exceeds a dynamic threshold.
-
Towards Consistent Video Geometry Estimation
One transformer, trained with random-sized temporal attention chunks, unifies offline, streaming, and long-video depth, normal, and point-map estimation and reports new best numbers on five public benchmarks.
-
UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.
-
Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation
Fine-tuning text-to-video models on sparse low-quality synthetic data for physical camera controls outperforms fine-tuning on photorealistic data.
-
TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration
TPGDiff introduces hierarchical triple-prior guidance in a diffusion network, placing degradation priors throughout, structural priors in shallow layers, and semantic priors in deep layers for improved all-in-one image restoration.