REVIEW 9 cited by
OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation
read the original abstract
Recent advancements in visual generation technologies have markedly increased the scale and availability of video datasets, which are crucial for training effective video generation models. However, a significant lack of high-quality, human-centric video datasets presents a challenge to progress in this field. To bridge this gap, we introduce OpenHumanVid, a large-scale and high-quality human-centric video dataset characterized by precise and detailed captions that encompass both human appearance and motion states, along with supplementary human motion conditions, including skeleton sequences and speech audio. To validate the efficacy of this dataset and the associated training strategies, we propose an extension of existing classical diffusion transformer architectures and conduct further pretraining of our models on the proposed dataset. Our findings yield two critical insights: First, the incorporation of a large-scale, high-quality dataset substantially enhances evaluation metrics for generated human videos while preserving performance in general video generation tasks. Second, the effective alignment of text with human appearance, human motion, and facial motion is essential for producing high-quality video outputs. Based on these insights and corresponding methodologies, the straightforward extended network trained on the proposed dataset demonstrates an obvious improvement in the generation of human-centric videos. Project page https://fudan-generative-vision.github.io/OpenHumanVid
Forward citations
Cited by 9 Pith papers
-
MBench: A Comprehensive Benchmark on Memory Capability for Video World Models
MBench is a new benchmark that quantifies long-term memory in video world models via three hierarchical consistency dimensions evaluated on curated real videos.
-
Ripple: Real-Time Streaming Audio-Video Generation With Cross-Modal Recurrent Memory
Ripple streams joint audio-video at ~28 FPS with long-form coherence via sliding-window attention, cross-modal recurrent memory, and a three-stage causal distillation plus RL recipe.
-
OmniMate: Open-Ended Real-Time Streaming Audio-Visual Generation for Interactive Avatars
OmniMate generates real-time streaming video, speech, and sound effects for interactive avatars while preserving the same face and voice across long multi-turn conversations.
-
InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars
InteractiveAvatar uses autoregressive distillation, Long-Short Visual Memory, and a Reasoning-Reaction Module to enable real-time, consistent, intent-aware avatar video streaming.
-
InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars
InteractiveAvatar is a real-time infinite-streaming avatar video generation system using autoregressive distillation, Long-Short Visual Memory for consistency, and a Reasoning-Reaction Module for intent-aware interactions.
-
StreamChar: Long-Horizon Streaming Character Audio-Video Generation with Decoupled Orchestration
StreamChar decouples LLM-based orchestration from DiT denoising to achieve real-time long-horizon streaming character audio-video generation with reduced drift and misalignment.
-
Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation
Unison presents a unified audio-video generation model that decouples speech and sound effects while using bidirectional forcing to synchronize with motion, claiming SOTA perceptual quality and alignment.
-
Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation
Unison introduces a unified framework using semantic-guided harmonization and bidirectional cross-modal forcing to generate human-centric videos with improved synchronization between motion, speech, and sound effects.
-
LPM 1.0: Video-based Character Performance Model
LPM 1.0 generates infinite-length, identity-stable, real-time audio-visual conversational performances for single characters using a distilled causal diffusion transformer and a new benchmark.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.