Organizations: School of Computer Science, Nanjing University. · SenseTime. · Shenzhen Technology University. · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology.
Generating physically plausible videos for solid-gas dynamics is challenging as different phases exhibit distinct dynamics yet remain coupled through physical interactions. We present PAVG, a Phase-Aware Video Generator for solid-gas dynamics and interactions. It employs a dual-branch architecture to explicitly model the distinct dynamics of solids and gases, while spatiotemporal cross-attention captures their physical interactions. This design enables PAVG to preserve phasespecific motion characteristics while producing physically consistent responses across phases. To facilitate this task, we further construct a simulation corpus comprising over 700K physical trajectories across diverse solid, gas, and solid-gas interaction scenarios. Extensive evaluations demonstrate that our PAVG produces videos with improved motion adherence, physical plausibility, and visual quality compared with existing approaches.
Figures & tables
Figure 1: A tennis ball moving through dust. Wan2.2 moves the ball in the wrong direction; Force Prompting leaves the dust stationary; FlashMotion moves the ball and dust together without a visible interaction. PAVG follows the prescribed ball motion and produces a responsive dust plume, yielding a more physically plausible solid–gas interaction. The figure contains fine-grained motion details and is best viewed on a computer.
Figure 2: PAVG overview: (a) initialize phase-specific 3D point clouds; (b) use a phase-aware point-trajectory predictor to predict solid and gas trajectories; (c) project the trajectories to guide a pretrained video generation model. Flames and snowflakes mark trainable and frozen modules.
Figure 3: PAVG architecture: (a) phase-specific denoising with cross-phase interaction (CPI) modules; (b) gated cross-attention within CPI; and (c) spatial and temporal attention axes. Hg′ denotes the gas latent feature after the CPI update.
Figure 4
Method
Overall ∗
Single-phase: Solid
Single-phase: Gas
Interaction: Solid–gas
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
Gas-only
SFBC
—
—
0.483874
0.120906
0.274170
—
DLF
—
—
0.483505
0.053650
0.241004
—
SEGNN
—
—
0.561893
0.007060
0.176081
—
Solid-only
Table 2: Trajectory prediction across single-phase motion and solid–gas interaction.
Solid
Predictor
vIoU ↑
CD ↓
Corr ↓
Solid-only
0.588372
0.022882
0.080403
Gas-only
N/A
N/A
N/A
Unified
0.571150
0.024851
0.086428
Table 3: Phase-specific versus unified modeling in Stage 1. Solid-only and Gas-only are trained separately; Unified shares one predictor across both phases. Training exposure is matched per phase. Solid scores aggregate the five solid subsets.
Figure 5: Qualitative interaction-pathway ablation on three held-out solid–gas cases. With the CPI pathway enabled, the gas visibly responds to and follows the solid’s motion.
Variant
Joint solid–gas
Interaction: solid
Interaction: gas
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
PAVG
0.690028
0.003928
0.059928
0.728056
0.011593
0.049734
0.642296
0.003537
0.070121
w/o solid-to-gas Cross-Attention
0.540755
0.016072
0.178830
0.728056
0.011593
0.049734
0.428435
0.067136
0.307926
Table 4: Quantitative interaction-pathway ablation on the solid–gas interaction subset of our benchmark. The same PAVG checkpoint is evaluated with or without solid-to-gas Cross-Attention. Metrics are reported for the concatenated joint output and separately for each phase.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Dataset
Simulator
Train
Test
Solid
elastic-force
MPM
150,201
100
elastic-gravity
MPM
94,038
100
plasticine-gravity
MPM
71,356
100
sand-gravity
MPM
95,414
100
rigid-gravity
rigid body
97,091
100
Appendix
Table 5: Training datasets and held-out trajectory test sets. The force condition denotes drag for solid sequences and initial velocity for gas sequences.
Setting
Value
Blocks / latent dimension / attention heads
8 / 256 / 4 per branch
Points / future frames
2,048 per phase / 24
Optimizer
AdamW, β=(0.9,0.999) , ϵ=10−8
Learning rate / weight decay
10−4 / 10−2
Schedule / warmup
Cosine decay / 100 updates
Gradient clipping / precision
Norm 1.0 / bfloat16
Appendix
Table 6: PAVG architecture and optimization settings.
Initialization
Overall ∗
Single-phase: Solid
Single-phase: Gas
Interaction: Solid–gas
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
vIoU ↑
CD ↓
Corr ↓
Random
0.644972
0.008163
0.082704
0.582268
0.022199
0.080927
0.710286
0.002036
0.096300
0.643667
0.004209
0.076795
Phase-specific
0.690964
0.006586
0.063135
0.617950
0.016784
0.067902
0.783433
0.001444
0.062611
0.681236
0.004059
0.061014
Appendix
Table 7: Stage 2 initialization ablation under the same dual-branch architecture and reduced-loss training recipe. Overall uses the weighting in Table 2 .
Objective
vIoU ↑
CD ↓
Corr ↓
w/o Lcoupling
0.677608
0.003948
0.061509
with Lcoupling
0.681236
0.004059
0.061014
Appendix
Table 8: Ablation of the cross-phase interaction physical constraint on the solid–gas interaction subset of our benchmark.
GPT-4o
GPT-5.5
GPT-5.6-sol
Gemini- 3.8-flash
DeepSeek- v4.1-flash
Method
SA ↑
PC ↑
VQ ↑
SA ↑
PC ↑
VQ ↑
SA ↑
PC ↑
VQ ↑
SA ↑
PC ↑
VQ ↑
SA ↑
PC ↑
VQ ↑
HunyuanVideo-1.5
3.63
3.63
3.88
3.06
2.91
3.59
2.94
2.75
3.69
3.00
3.00
3.63
2.94
2.88
3.53
Wan2.2-I2V-A14B
3.31
3.50
3.81
3.16
3.19
3.44
2.53
2.41
3.34
2.94
3.00
3.53
3.31
3.28
3.53
CogVideoX1.5-5B-I2V
2.75
2.94
3.41
2.13
2.31
2.72
1.84
2.19
2.75
2.31
2.47
2.91
2.25
2.41
2.84
DragAnything
2.78
2.81
3.06
2.38
2.31
2.50
1.94
2.09
2.41
2.34
2.44
2.72
2.56
2.59
2.78
ObjCtrl-2.5D
2.59
2.66
3.03
2.09
2.03
2.75
2.16
2.06
2.75
2.50
2.53
2.88
2.03
2.06
2.72
Appendix
Table 9: Individual judge scores over the same 32 scenes. Higher is better for all metrics. Bold : best; underlined : second best within each judge and metric.