Pith. sign in

REVIEW 11 cited by

DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2506.10568 v2 pith:UBBHENJ6 submitted 2025-06-12 cs.CV cs.AI

Lizhen Wang , Zhurong Xia , Tianshu Hu , Pengrui Wang , Pengfei Wei , Zerong Zheng , Ming Zhou , Yuan Zhang
show 1 more author
Mingyuan Gao
This is my paper
classification cs.CVcs.AI
keywords human-productdemonstrationproductdiffusiondreamactor-h1generatinghigh-fidelityhumans
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In e-commerce and digital marketing, generating high-fidelity human-product demonstration videos is important for effective product presentation. However, most existing frameworks either fail to preserve the identities of both humans and products or lack an understanding of human-product spatial relationships, leading to unrealistic representations and unnatural interactions. To address these challenges, we propose a Diffusion Transformer (DiT)-based framework. Our method simultaneously preserves human identities and product-specific details, such as logos and textures, by injecting paired human-product reference information and utilizing an additional masked cross-attention mechanism. We employ a 3D body mesh template and product bounding boxes to provide precise motion guidance, enabling intuitive alignment of hand gestures with product placements. Additionally, structured text encoding is used to incorporate category-level semantics, enhancing 3D consistency during small rotational changes across frames. Trained on a hybrid dataset with extensive data augmentation strategies, our approach outperforms state-of-the-art techniques in maintaining the identity integrity of both humans and products and generating realistic demonstration motions. Project page: https://lizhenwangt.github.io/DreamActor-H1/.

Discussion (0). Sign in to comment.

Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos

    cs.CV 2026-01 unverdicted novelty 7.0 of 10

    CoMoVi co-generates 3D human motions and 2D videos synchronously in a single diffusion denoising loop using 3D-to-2D projection and dual-branch diffusion with 3D-2D cross attentions.

  2. AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via Implicit Representation Alignment

    cs.CV 2026-07 conditional novelty 6.0 of 10

    AgentHOI generates human-object interaction videos from text plus one human image and one object image, using multi-agent action planning and implicit text-to-motion feature alignment inside a video diffusion model.

  3. StreamHOI: Interaction-aware Temporal Memory Adaptation for Streaming HOI Video Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A streaming image-to-video model that assigns block-specific sink/local memory and learned RoPE distance scaling preserves long human-object interactions under bounded latency.

  4. HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enhancement

    cs.CV 2026-07 conditional novelty 6.0 of 10

    HOMIE unifies inter- and intra-subject video personalization by injecting MLLM-derived relational features into DiT self-attention (GMG) and tagging tokens with modality/reference embeddings (MRE), reporting SOTA on a...

  5. CoInteract: Physically-Consistent Human-Object Interaction Video Synthesis via Spatially-Structured Co-Generation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    CoInteract adds a human-aware mixture-of-experts and spatially-structured co-generation to a diffusion transformer to synthesize videos with stable structures and physically plausible human-object contacts.

  6. OmniShow: Unifying Multimodal Conditions for Human-Object Interaction Video Generation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    OmniShow unifies text, image, audio, and pose conditions into an end-to-end model for high-quality human-object interaction video generation and introduces the HOIVG-Bench benchmark, claiming state-of-the-art results.

  7. MVHOI: Bridge Multi-view Condition to Complex Human-Object Interaction Video Reenactment via 3D Foundation Model

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Using a 3D foundation model to produce viewpoint-aware anchors plus multi-view reference textures enables realistic human-object-interaction reenactment with large out-of-plane rotations.

  8. HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion

    cs.CV 2026-02 conditional novelty 6.0 of 10

    HairWeaver animates a single human photo with physically plausible hair motion by transferring simulated CG hair dynamics into a frozen video diffusion model via two lightweight LoRA adapters.

  9. HOComp: Interaction-Aware Human-Object Composition

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A diffusion-transformer method that composes a foreground object into a human image with MLLM-chosen interaction regions, pose keypoint supervision, and appearance/background consistency losses, plus a new paired dataset.

  10. Real-Time Human-Centric World Modeling for Upper-Body Human-Object Interaction

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A distilled video model jointly controls multi-scale upper-body motion latents and two discrete contact states to generate real-time human–object interaction at 25 FPS.

  11. DreamSwapV: Mask-guided Subject Swapping for Any Customized Video Editing

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    DreamSwapV performs mask-guided, subject-agnostic subject swapping in videos using multiple conditions, an adaptive mask strategy, and a new benchmark.

Pith tools