Impact sound rendering synthesizes the sound produced when a 3D object is struck, but practical renderers often rely on fixed material presets such as wood, plastic, or steel. These presets limit the range of impact sounds a renderer can express, while manually adjusting the underlying material parameters remains difficult without expertise in material acoustics. We therefore study inverse impact sound rendering: predicting material parameters from a reference impact sound so that a simulator can recreate a similar material response. To support this task, we introduce ImpactMat, a dataset and benchmark of single and blended material impact sounds paired with ground-truth material parameters. We further propose a feed-forward model that predicts these parameters from one or more recordings, using blended materials to learn smooth transitions between material types. Experiments show that our method outperforms competitive baselines and enables re-rendering from real recordings without manual parameter tuning. The project page is available https://material-from-impact.github.io/material-from-impact/.
Figures & tables
Figure 1 : Inverse impact sound rendering from reference audio. Given a reference impact sound from an object with unknown material, our continuous material estimator predicts five renderer-compatible parameters, which are then used with a target mesh for physics-based sound synthesis. This enables re-rendering with a similar material response on a new object.
Figure 2 : Overview of the proposed continuous material estimator. Multiple impact recordings are encoded, pooled as an unordered set, and mapped to continuous material parameters. Auxiliary classification and blend-consistency losses guide training, while inference uses the direct regression head.
Split
Objects
Single
Blend
Total
Train
880
7,040
63,360
70,400
Val
110
880
7,920
8,800
Test
110
880
7,920
8,800
All
1,100
8,800
79,200
88,000
Table 1 : ImpactMat dataset and split statistics. Single-material data are synthesized for each object using eight base materials. Blended-material data cover 24 selected material pairs at three mixing ratios, yielding 72 blend groups per object. Each impact group contains eight recordings.
Method
Setting
Test subset
ρ
E
ν
α
β
Avg. NMAE
Infer. time
VRAM
DSP + Ridge Reg.
Conventional
Single
0.190
0.443
0.047
9.93
3.26×10−7
1.405
16.1 (ms)
CPU
Blend
0.131
0.321
0.032
5.19
1.83×10−7
0.961
Qwen2.5-Omni-7B [ 21 ]
ICL
Single
0.338
0.614
0.071
13.38
1.61×10−1
1.270
5.6 (s)
16.1 GB
Blend
0.257
0.495
0.067
8.23
1.73×10−1
1.162
Audio Flamingo 3 [ 8 ]
ICL
Single
14.1
2.14
0.130
21.7
1.8×10−2
12.42
4.4 (s)
18.3 GB
Blend
13.4
2.33
0.123
11.3
4.4×10−2
10.92
Table 2 : Comparison of physical-parameter estimation methods. Results on ImpactMat. ρ and E are reported in the log-space target scale, ν is linear, and α and β in physical units. Avg. NMAE is computed over all five normalized target dimensions and is omitted for DiffSound, which estimates only E and ν in our evaluation. Evaluated with K=1 , lower is better.
Variant
Reg.
Cls.
Blend
Single
Blend
All
-
✓
0.0520
0.1053
0.0999
-
✓
✓
0.0648
0.1045
0.1005
-
✓
✓
0.0527
0.1032
0.0981
Ours
✓
✓
✓
0.0600
0.0989
0.0950
Table 3 : Ablation of training objectives for continuous estimation. Checkmarks indicate the loss terms used during training; Single, Blend, and All report NMAE on the corresponding test subsets. Evaluated with K=4 , lower is better.
K
Single
Blend
All
1
0.0784
0.1163
0.1124
2
0.0672
0.1043
0.1006
4
0.0600
0.0989
0.0950
8
0.0582
0.0965
0.0926
Table 4 : Effect of multi-impact aggregation at inference. Aggregating multiple reference impacts improves robustness to contact variation. Values report NMAE over the five material parameters. Lower is better.
Figure 3 : Spectrogram comparison for re-rendering validation. Each column compares an input reference with the sound synthesized after feeding the estimated parameters back into the simulator.
Domain
Condition
Sim.%
Unsure
Diff.
Synthetic
Re-render (ours)
100%
0%
0%
Real
G.T. variant
59.0%
17.0%
24.0%
Re-render (ours)
63.3%
20.8%
15.8%
Table 5 : Listening study for perceptual material similarity. Comparing reference and re-rendered impact sounds.
Figure 4 : Material transfer from reference audio to new geometry. Estimated material parameters are transferred to the same target mesh to synthesize ceramic-, plastic-, steel-, and glass-like impact responses.