Organizations: the School of AIA, Huazhong University of Science and Technology, Wuhan 430074, China · the S-Lab, Nanyang Technological University (NTU), Singapore 639798 · the Beijing Academy of Artificial Intelligence (BAAI), Beijing 100083, China · Zhejiang University (ZJU), Hangzhou 310058, China · the Institute for AI Industry Research (AIR), Tsinghua University (THU), Beijing 100084, China
Synthesizing physically plausible articulated human-object interactions (HOI) without 3D/4D supervision remains a fundamental challenge. While recent zero-shot approaches leverage video diffusion models to synthesize human-object interactions, they are largely confined to rigid-object manipulation and lack explicit 4D geometric reasoning. To bridge this gap, we formulate articulated HOI synthesis as a 4D reconstruction problem from monocular video priors: given only a video generated by a diffusion model, we reconstruct a full 4D articulated scene without any 3D supervision. This reconstruction-based approach treats the generated 2D video as supervision for an inverse rendering problem, recovering geometrically consistent and physically plausible 4D scenes that naturally respect contact, articulation, and temporal coherence. We introduce ArtHOI, the first zero-shot framework for articulated human-object interaction synthesis via 4D reconstruction from video priors. Our key designs are: 1) Flow-based part segmentation: leveraging optical flow as a geometric cue to disentangle dynamic from static regions in monocular video; 2) Decoupled reconstruction pipeline: joint optimization of human motion and object articulation is unstable under monocular ambiguity, so we first recover object articulation, then synthesize human motion conditioned on the reconstructed object states. ArtHOI bridges video-based generation and geometry-aware reconstruction, producing interactions that are both semantically aligned and physically grounded. Across diverse articulated scenes (e.g., opening fridges, cabinets, microwaves), ArtHOI significantly outperforms prior methods in contact accuracy, penetration reduction, and articulation fidelity, extending zero-shot interaction synthesis beyond rigid manipulation through reconstruction-informed synthesis.
Figures & tables
Fig. 1: ArtHOI recovers zero-shot articulated human-object scene geometry and dynamics from monocular video priors without 3D supervision. Unlike prior works ( e.g. , TRUMANS, ZeroHSI), our method achieves all four capabilities simultaneously: RGB rendering, articulated object modeling, physical constraint modeling, and zero-shot generalization, notably without using 3D supervision.
TABLE I: Comparison of capabilities across different approaches. HOI : Human-object interaction; RGB : RGB rendering; Art : Articulated objects; Phy : Physical constraints; ZS : Zero-shot.
Fig. 2: ArtHOI synthesizes 3D articulated interactions by reconstructing 4D scenes from monocular video priors. Stage I reconstructs object articulation with kinematic constraints. Stage II refines human motion under the reconstructed geometry.
Fig. 3: Key components for articulated interaction under monocular supervision. (a) Back projection maps masks to 3D to identify moving parts. (b) Quasi-static point pairs link dynamic/static regions for kinematic stability. (c) Contact loss projects 2D keypoints into 3D using object depth, guiding human motion without multi-view cues. Ablations in Figure 5 (middle: (b), right: (c)).
Fig. 4: Qualitative comparison of our method with baselines. Our method synthesizes more realistic articulated human-object interactions with proper contact and natural motion coordination. Better inspected in our supplementary video.
TABLE II: Comparison of interaction quality. Smoothness (↓) is best interpreted among zero-shot methods. Non-zero-shot high smoothness stems from minimal contact, not motion instability.
TABLE IV: Rigid object comparison under monocular RGB.
Fig. 5: Comparing our full model with variants. Better inspected in our supplementary video.
Method
Realism ↑
Contact Quality ↑
Motion Smoothness ↑
Overall Preference ↑
Ours vs. TRUMANS [ 4 ]
96.08%
98.00%
92.16%
98.04%
Ours vs. CHOIS [ 1 ]
95.20%
89.08%
94.83%
95.28%
Ours vs. LINGO [ 3 ]
90.20%
87.13%
92.00%
91.51%
Ours vs. ZeroHSI [ 6 ]
91.18%
85.41%
84.21%
89.42%
TABLE V: User study shows the percentage of participants who preferred our method over each baseline across four evaluation dimensions.
Method
Interaction
Articulation
X-CLIP ↑
Foot Sliding ↓
Contact% ↑
Rot (mean) ↓
Rot (max) ↓
Rot (min) ↓
Joint Opt.
0.187
0.67
61.45
12.34
35.89
2.01
w/o La
0.223
0.42
68.75
15.67
42.18
4.56
w/o Lk
0.201
0.58
59.82
6.71
21.41
0.58
w/o Ls
0.218
0.49
65.43
8.23
25.45
0.79
Full Model
0.244
0.31
75.64
6.71
21.41
0.58
TABLE VI: Ablation study results. We remove individual components and evaluate their impact on both interaction and articulation.
Fig. 6: Failure cases. Co-tracker struggles with low-texture or reflective regions, leading to distortions that propagate into articulation prediction.