EnvSocial-Diff: A Diffusion-Based Crowd Simulation Model with Environmental Conditioning and Individual-Group Interaction
Authors: Bingxue Zhao
Qi Zhang
Hui Huang
Venue: ICLR 2026 Poster (2026)
Keywords: Crowd simulation
Social physics force
Diffusion model
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A diffusion-based crowd simulation model with environmental conditioning and individual-group interaction.

Abstract: Modeling realistic pedestrian trajectories requires accounting for both social interactions and environmental context, yet most existing approaches largely emphasize social dynamics. We propose EnvSocial-Diff: a diffusion-based crowd simulation model informed by social physics and augmented with environmental conditioning and individual-group interaction. Our structured environmental conditioning module explicitly encodes obstacles, objects of interest, and lighting levels, providing interpretable signals that capture scene constraints and attractors. In parallel, the individual-group interaction module goes beyond individual-level modeling by capturing both fine-grained interpersonal relations and group-level conformity through a graph-based design. Experiments on multiple benchmark datasets demonstrate that EnvSocial-Diff outperforms the latest state-of-the-art methods, underscoring the importance of explicit environmental conditioning and multi-level social interaction for realistic crowd simulation.

Published as a conference paper at ICLR 2026
EnvSocial-Diff: A Diffusion-Based Crowd Simulation Model with Environmental Conditioning and Individual- Group Interaction
Bingxue Zhao Qi Zhang" Hui Huang
VCC, College of Computer Science and Software Engineering, Shenzhen University 24101030300ma 1s. szu. edu.cn, cn, gi.zhang. opt il. com, hhzhiyan gmail com
ABSTRACT
Modeling realistic pedestrian trajectories requires accounting for both social in- teractions and environmental context, yet most existing approaches largely em- phasize social dynamics. We propose EnvSocial-Diff: a diffusion-bassd crowd simulation model informed by social physies and augmented with environmen- tal conditioning and individual-group interaction. Our structured environmental conditioning module explicitly encodes obstacles, objects of interest, and lighting levels, providing interpretable signals that capture scene constraints and attractors. parallel, the individual -goup interaction module goes beyond individual-level modeling by capturing both fine-grained interpersonal relations and group-level conformity through a graph-basee design. Experiments on multiple benchmark datasets demonstrate that EnvSocial- Diff outperforms the latest state-of ofre-offthh state-of-the-art methods, underscoring the importance explicit environmental conditioning and multi-level social interaction for realistic crowd simulation.
INTRODUCTION
Crowd simulation plays an important role in model- ing and predicting the collective behavior of pedes- trians in dynamic environments, with applications ranging from virtual reality and digital twin systems to public safety management and urban planning (Musse al. 2021). A central goal is gener- ate realistic walking trajectories for multiple agents while accounting for social interactions and envi- ronmental constraints. Over the years, numerous methods have been proposed to address this task. ranging from rule-based approaches (Reynolds 1987) and force-based models (Helbing & Molnar 2021/Chraiiii Chraibi et al.] to
001
Streel
C: Obstacle
Group
Pedastrian
Lightate conditions
Neighbor
Dark
Bright
Figure 1: Environmental factors are important in crowd simulation. The target pedestrian influenced by nearby neighbors, obstacles, objects of interest (OOI), and lighting condi- tions. The scene image is divided into grids to calculate lighting information.
1995 Kolivand et al. [202
data-driven learning- based approaches (Alahi al.)
2016 Gupta et al. 2018 Lee et al.] 2018: Char- alambous et al. 2023). Among them, the Social Force Model (SFM) (Helbing & Molnar 1995) and its extensions have been widely adopted for their interpretable and physically grounded structure. More recently, physics-infommn informed generative ap- proaches have emerged. In particular, the Social Physics Informed Diffusion Model (SPDiff) (Chen) al. 2024) integrates conditional diffusion process into the Social Force Model, where the dif- fusion module refines predicted accelerations based on historical motion and individual-level social interactions. While SFM inherently accounts for basic obstacle avoidance through repulsive forces, SPDiff does not explicitly incorporate structured environmental conditioning (e.g., objects of in-
"Corresoonding author.

---

Published as a conference paper at ICLR 2026
terest or lighting), nor does it capture group-level conformity, leaving important behavioral factors
underexplored.
A core challenge in crowd simulation lies in capturing the diverse factors that shape pedestrian be- havior, including social interactions such as collision avoidance, group coherence, and route choice.
While most existing approaches (Mohamed al. 2020 Xu al. 2022 Kim et al. 2024 Itatani & Pelechano 2024
Pascoli et al. 2025) have primarlly focused on modeling these interactions through graph-based, recurrent, or probabilistic frameworks, they typically exhibit two major limitations. First, social modeling is often restricted to individual-level interactions, overlooking higher-level group conformity that strongly influences collective motion. Second, the treatment the environ- ment is oversimplified: most methods, including physics -insssemfformed models such as SPDiff, primarily account for obstacles through repulsive forces or binary traversable maps, but do not explicitly en- code richer contextual cues. This abstraction neglects influences such as attractive objects of interest (OOI) (e.g.. stores, kiosks), which act as behavioral attractors guiding route choice (Tong Bodej 2022 and perceptual cues like lighting (as illustrated in Figure [ which have been shown in psychology and urban design studies to affect safety perception, comfort, and walking preferences Warren et al. 2001 Hao al. 2022; Liu et 2022). Addressing these gaps motivates the need for a unified framework that explicitly integrates structured environmental conditioning with multi-level
social modeling.
To overcome these limitations, we propose Env-ociallDiff" (see Figure 2 a social physics-informed diffusion model that jointly models structured environmental conditioning and individual-group in- teraction. On the environment side, we encode obstacles, objects of interest (OOI), and lighting as conditional signals that guide the generative denoising process. On the social side, an Individual- Group Interaction module captures both individual-level relations and group-level conformity via graph-based design. These two pillars are fused with historical trajectories and a destination at- traction term, forming four complementary components that jointly condition the diffusion model to produce socially compliant, context-aware, and realistic trajectory predictions.
In summary, the paper's contributions are as follows.
We propose EnvSocial-Diff, diffusion based crowd simulation model informed by so- cial physics, which unifies structured environmental conditioning with social interaction modeling.
We design structured environmental encoders that explicitly model obstacles, objects of interest, and lighting, and integrate them with an Individual-Group Interaction (IGI) mod- ule that captures both fine-grainee interpersonal relations and group-level conformity. This unified design enables physically interpretable trajectory predictions.
Experiments GC and UCY benchmarks show that EnvSocial-DDe outperforms state-of- the-art baselines across multiple trajectory metrics, validating the effectiveness of explicit environmental conditioning and multi-level social interaction modeling.
2
RELATED WORK
Physics-based Crowd Simulation. Early approaches relied on handcrafted rules and physics- inspired models. The Boids model (Reynolds) 1987) simulated collective animal motion through simple rules of separation, alignment, and cohesion. A milestone, the Social Force Model (SFM) (Helbing & Molnar 1995), introduced psychological forces such as goal attraction and social re- pulsion, enabling realistic simulation of pedestrian interactions. Other approaches include Cellular Automata (CA) (Sarmady et al. 2010). which discretize time and space for efficient simulation but lack motion continuity. and Velocity Obstacle (VO) methods (Fiorini & Shiller 1998) and their variants (RVO (Van den Berg et al.] 2008), ORCA (Snape et al.J 2010), HRVO (Van Den Berg al. 2011), which use geometric constraints for collision avoidance. Inspired by fluid dynamics, continuum- based models (Hughes) 2002; Huang et al.) 2009; Liang & Dul (021) treat crowds as continuous media, capturing macroscopic flow in dense settings. While these methods laid founda- tional groundwork, their reliance on predefined rules and physical simplifications limits their ability to model complex, context-aware human behaviors.
Data-driven Social Mudeling. Recent methods leverage learning- based frameworks to capture pedestrian interactions. Early works such Social LSTM (Alahi al. 2016) and Social GAN
2

---

Published as a conference paper at ICLR 2026
Table
Summary of main notations used in EnvSocial-DDe Difff
Symbol
Meaning
Symbol
Meaning
1
Pedestrian indices
Time step index
St
State of i at [pt, ot
H
Prediction horizon (number of future steps)
P
Position of time
Velocity of time &
Acceleration of at time f
fa
Denoising network in acceleration space
F dest
Destination-driving force Environment-induced force on History- based force on i Noisy acceleration at step k Set of obstacles Set of lighting cells
cf
Conditioning signals [F. @ social @ hist)
F en
F. Fsocial
Social interaction force on
F hia
Diffusion step index (l,..., K)
Yik
yi,0
Clean target acceleration of at time t of objects interest Environment entities, M OUZUR
M
f
Global scene feature
light
Spatial lighting vector
ft
Feature of the I-th obstacle
for
Feature of the m-th object-of inteerrfstttttttteeeeeeeee (00I)
sim
Approach tendency
sime
Motion alignment
sim
Group conformity
Ts3 Relative motion descriptor (Apt @
Gupta et al. 2018) employed recurrent and generative models for individual-level interaction mod- eling, while STGCNN (Mohamed et 2020) introduced spatio-temporal graphs for relational rea- soning. Subsequent approaches, including SocialCircle (Wong et al. 2024). HighGraph (Kim et al.) 2024], and RSBS (Sun et al., 2020), enhance modularity and temporal dynamics. Other directions explore interpretable latent modeling (SocialVAE (Xu et al. 2022)), endpoint conditioning (PECNet
[Mangalam et al. 2020)), and multimodal diffusion- prediction (MID (Gu et al. 2022
In parallel, some methods attempt to integrate scene context. Scene-aware approaches leverage mantic maps or global embeddings (Manh Alaghband] 2018 Mangalam al. 2021; Ngiam al.] 2022 Bac al. 2025 Yuan et al. 2021). which provide high-level semantic awareness but lack be- havioral modeling. NSP (Yue et 20022) which we also include as baseline in our experiments, introduces physics-inspired framework that fuses social interactions with environmental cues. In NSP, the environment is represented by segmenting scenes into walkable and non-walkable regions, where non-walkable abstraction overlooks richer environmental roles such as attractive objects of interest (OOI) or per- ceptual like lighting, and its social modeling remains limited to the individual level. UniTraj (Feng et al. 2024) further proposes unified environmental network for short-horizon trajectory prediction. similarly relies on global semantic context rather than structured environmental conditioning. Overall, these environment-aware approaches are confined to short-term forecasting and not integrate multi-level social modeling, leaving factors underexplored.
pedestrian's field of view exert repulsive forces. However, this binary
Physics-informed Generative Approaches. To improve long-term prediction, physics-infommn generative methods combine physical priors with data-driven learning. PCS (Zhang et al. 2022) integrates physical constraints with trajectory forecasting, while SPDiff (Chen et al. 2024 intro- duces diffusion model conditioned on social forces derived from the Social Force Model (SFM). In SPDiff, the diffusion process refines accelerations based historical motion and individual- level interactions. While the SFM formulation inherently includes obstacle avoidance, SPDiff does not explicitly incorporate structured environmental conditioning (e.g., OOI, lighting) group-level conformity, leaving important behavioral influences underexplored.
In contrast, work introduces structured environmental ciniiiooinn-decomppon the environ- ment into obstacles, O0I, and lighting-and complements it with an Individual-Grun Interactions module, enabling unified modeling of environmental and social influences for realistic long-horizon crowd simulation.
METHOD
We propose EnvSocial-Diff' (see Figure diffusion-basse crowd simulation model informed by social physics. Following SPDiff (Chen et al., 2024), the destination attraction force is applied outside the diffusion process, thereby preserving long-term intent, while historical trajectories

---

Published as a conference paper at ICLR 2026
C
Ruvironmental Conditioning
Individual Group Interaction
Historical Trajectories
Obvtacless
ODI
Score
50%
smmg plm;
Lightnn
Kigke
sampled moise N(0.1)
MLP ghiis
Concatenation
psocial
Denoise (step K)
Vio
Add
a
Pidess
Cross Attonion
Cross Allaniion praniat
Destination
p
1/t
Figure 2: EnvSocial-Diff pipeline. Pedestrian motion is modeled as in the Social Force Model (SFM), where the destination force Fides is applied outside the diffusion process to preserve long- term intent. The conditioning signals di [F + F social @ Fhhis] aggregate three interactive components: (1) Environmental Conditioning - obstacle and O0Í features are encoded via cross- attention with pedestrians, while lighting features extracted from grid-based scene brightness; (2) Individual-Grood Interactions GNNs encode individual-level (sim!;. sim?y), group-level (sim)), and relative motion (riy) to produce the social force F sociall and (3) Historical Trajectories ssort-term motion trends encoded from recent states using LSTM. Given ot and Gaussian noise E ~ (0, 1), the denoiser fe performs reverse diffusion to recover clean accelerations y which are then combined with the destination force yield the final prediction ā
encoded via a unidirectional LSTM. Unlike SPDiff, which incorporates environmental condition- ing only implicitly via the classical Social Force Model (SFM) formulation, explicitly model structured environmental factors through obstacles, objects of interest, and lighting. Further- more, we introduce Individual-Gront Interaction (IGI) module that captures both individual- level relations and group-level conformity. Together, these history-constitute the conditioning signals oi the diffusion model, while the destination force is injected during rollout. This design enables trajectory forecasts that are context-aware, socially compliant, and physically interpretable.
IGI, and
The model takes as input the 2D start and destination coordinates of each pedestrian, together with the scene environment information. The environment consists of the BEV image and its textual description, together with structured environment entities M (summarized in Table For obstacles O and objects of interest I, each entity represented by its cropped image patch, textual description, and 2D position in the BEV frame. For the lighting factor L, we extract the V-channel from the BEV image in HSV space to form a global lighting map, which serves as the lighting input. Given these inputs, the model generates full trajectories for all pedestrians by predicting their accelerations over time.
3.1 OVERALL MODEL
As shown in Eq. ( pedestrian motion follows the SFM (Helbing & Molnar 1995), where multiple interactive forces jointly govern pedestrian dynamics:
F.
Frdes
(Fihiss
Σ
F
social
Σ BEM
Feavy
jENesgh.
conditioning signals
Here, Fidest drives pedestrian i toward its destination and is defined following SFM as Fddest
my U. where v; is the current velocity, i/ the desired walking speed, and n. is the direction
4

---

Published as a conference paper at ICLR 2026
towards the destination. m, is coefficient for individuals while μ is a global coefficient. Unlike the other forces, this term is applied outside the diffusion process during rollout to preserve long- term intent. The remaining terms constitute the conditioning signals: Fi encodes short-term motion trends from recent trajectories, Σ EjeNeigh, Fi models individual-group interactions, and ΣMeM Fi incorporates structured scene context (M = O OUZUC for obstacles, objects of interest, and lighting).
As acceleration is proportional to the net force (F = mā), we predict future accelerations rather than positions. This yields physically grounded representation of motion dynamics. To model this process, we employ a diffusion model (Ho et al. 2020) within the acceleration space. Specifically, the conditioning signals in Eq. (I) are modeled by the denoiser output. The destination force is then added outside the diffusion process to obtain the final acceleration. The state pedestrian at time t is denoted as S = pt, o at denote position, velocity, and acceleration, respectively. Our goal is 3.+H } over the prediction horizon H. to generate the sequence of predicted accelerations (ā++1 d;
In the forward process, we progressively add Gaussian noise to the ground-truth accelerations y forming Markov chain that transforms the clean accelerations into approximately pure noise:
g(yi,k Yi,k-1) = -V 10--88840 Y 3x1)
where 8& denotes the variance at step k in the noise schedule, with k = 1, K.
During inference, the reverse process starts from Gaussian noise and iteratively denoises it into accelerations conditioned on of = eF e Froccial @ F.hia. A neural network fe parameterizes this process by predicting clean accelerations at each step conditioned on ct.
po yt,. Vi... k c,))
To encode hist we use the recent trajectory sequence (5t-L+1 ,..., St
over an observation win- dow of length L. The sequence is passed through linear projections and a unidirectional LSTM encoder, and the final hidden state provides a compact temporal feature for conditioning the de- noiser (see Figure 2 Historical Trajectories block).
After obtaining the denoised conditioning accelerations Voo- add the destination force Fi to produce the final predicted accelerations ă. These accelerations are then used to recursively update the velocity and position of each pedestrian via standard kinematic equations.
t ++--- S4+T At.
(4)
p! p. + ह. At + 18/tr At
(5)
where T = I,..., H and At denotes the time step.
3.2 STRUCTURED ENVIRONMENTAL CONDITIONING
The environmental conditioning module explicitly encodes structured scene elements--includig obstacles, objects of interest (0OI, e.g., stores, kiosks, benches), and lighting- in the Environ- mental Conditioning block of Figure 2 Obstacle and OOI features are first enhanced with global scene context, after which they interact with pedestrians via cross-attention, while lighting features are extracted from grid grid-based brightness in the HSV space. Together, these features provide repul- sive, attractive, and contextual cues that influence pedestrian motion.
For obstacles and objects of interest (OOI), GPT is first used to produce concise textual descriptions. The scene-level BEV image and the cropped image patches are encoded using ResNet-50 to obtain visual embeddings, while the textual descriptions are encoded using BERT. The resulting visual and textual embeddings are then concatenated and projected to respectively form the global scene feature fec, the obstacle features fohh, and the OOI features f....
Obstacles. We model obstacle influence in two stages of cross-attention. First, each obstacle feature p enhanced using the global scene feature fsc;
Do PJojq([! 4 pt. (
Keec
Projx 8ec (
5

---

Published as a conference paper at ICLR 2026
ft Attention(Q! K V*0,
where 14th is the obstacle position. This produces context-enhanced obstacle features fo Second, pedestrians attend to the enhanced obstacle features to capture obstacle setttacle-edestrian interac- tions:
Q Kgbs vdi
spod-obs -E
softmax
b(p%.))
Vi00s
IEO
where Q: = SQ encodes pedestrian i's state S [P t7 T.) Ki W fios Vi = Wv fros nel
Pi. Pt ohs p; is the relative position, b(-) is small neural network, and d1 is the feature dimensionality for scaling.
Objects of Interest (OOI). OOI serves as semantic attractor that influences route choice. Unlike obstacles, which require fine-grained avoidance behavior, OOI primarily provides global semantic cues. Therefore, each OOI feature m enhanced by concatenating its positional encoding pm and the global scene feature fae, followed by projection:
pooi Proj(Jm @ Pm () (CC).
The interaction with pedestrians is then modeled via cross-attention:
[ Km
pped voi
Σ
softmaxm
+ b(prt))
Vini
(10)
vd2
MEZ
where Q Wq K network, and d2 is the feature dimensionality for scaling.
W Pool
Vool
W Pi
prol
F.. b(.) is small neural
Lighting. We treat lighting as a global contextual factor rather than localized entities. The scene image is divided into grids, and the average value of the V-channel in HSV space within each cell is
pooled form a spatial lighting vector fraw which is then encoded by lightweight MLP, resulting into the lighting feature light
penc
MLP((iig)))
(11)
light
This design is supported by prior psychophysical evidence showing that lighting strongly influences pedestrian movement (Rahm & Johansson 2018), which reports that outdoor lighting improves walkability and facilitates obstacle detection.
Pedestrian--Eviiromenn Feature Aggregation. Finally, the influences from obstacles, 00I, and lighting are aggregated into a unified environment- aware feature for pedestrian i by concatenation followed by an MIP:
F eav
MLP
sped-ots
f
ppod-ooi
fight
(12)
3.3 INDIVIDUAI-GRRUP INTERACTION (IGI)
The IGI module encodes social influences at two levels, as illustrated in the IGI block of Figure At the individual level, similarity measures capture approach tendency and motion alignment between pedestrian i and its neighbors j E Neigh; At the group level, conformity measure models the alignment with the surrounding group conformity. In addition, relative motion descriptors rij = Api @ Ade provide complementary spatial and velocity cues. These descriptors are aggregated by multi-layer GNN produce the social force feature Fsociall which serves as part of the conditioning input.
Individual-level similarities. We introduce two measures to capture pairwise relations between pedestrian i and neighbor j:
Approach tendency simly quantifies whether is moving toward i, reflecting potential collision risk:
sim!, |
Ap.
+1)
(13)
where Ap - This is the cosine similarity between the normalized relative position and the neighbor's velocity, mapped to [0, 1]. Larger values indicate that j is approaching more directly.

---

Published as a conference paper at ICLR 2026
Motion alignment simjy measures the directional consistency of their velocities:
sim
+1)
(14)
Higher values indicate stronger velocity alignment.
Group-level similarity. Beyond individual relations, pedestrians are influenced by the collective motion of surrounding neighbors. We define group conformity similarity sim? by comparing the motion state pedestrian with the neighborhood average:
sim
(15)
where w; =;=0; a, encodes the velocity and acceleration of i, and 9: Weigh, EjeNeigh, (T, i dj) denotes the average motion its neighbors, and [Weigh,| is the number of neighbors. This similarity, normalized (0, 1], reflects the degree which pedestrian i conforms to the surrounding group dynamics; larger values indicate stronger conformity.
GNN Aggregation. To instantiate the social force Fssocial in Eq. 11. we employ a multi-layer graph neural network (GNN) that aggregates the individual-level and group-level similarities de- fined above. Each pedestrian is represented as a node, initialized as:
hf MLP inn (S) e (:)::
(16)
where S - -p is the pedestrian's state, ei denotes sampled noise, and g: encodes the neigh- borhood average motion. At each GNN layer 1g. the edge feature between pedestrian and neighbor j incorporates relative motion and the similarity measures:
Eij rij @ simty @ sim @ sim
(17)
where Tij denotes the relative motion descriptor. These edge features capture spatial proximity, motion cues, and social affinity, and are transformed by a shared edge-level multilayer perceptron MI.Pagee generate messages.
Each node updates its representation by concatenating its current hidden state n. the mean- aggregated messages from neighbors, and the normalized local group feature N Norm(g:). followed by node-level transformation:
No+1=
+1 MLP node n |eighh
Σ
MLPedge((s)) @
(18)
jENeigh:
This updated hidden state is progressively refined through T.y layers within the IGI module. Finally, task-specific output MLP predicts the social interaction force for pedestrian
succial MLP out (hu(h (1
(19)
3.4 DENOISING AND MULTI-FRAME ROLLOUT TRAINING
After reverse diffusion, the denoised conditioning acceleration oi is combined with the destination term to obtain the final acceleration â We train the model under multi-frame rollout strategy (Chen al. 2024), and optimize weighted mean-squared error over accelerations and positions:
7 Ap 155
(20)
NH
where a,'T,, ",ttp are ground truth, à, d0tr ge+r are predictions, N is the number of pedestrians, and Xa; Ao are loss weights.

---

Published as a conference paper at ICLR 2026
Table 2: Quantitative comparison on GC and UCY datasets. Results for baselines are directly re- ported from SPDiff (Chen et al. 2024), except for E-V²-SC SSC and Ours, which are reproduced under the same experimental settings. Here, Ours corresponds to our proposed EnySocial- -Diff' model.
GC
UCY
Group
Models
MAEL QT
FDEL
MMDJ DTW
Coll
MAE] OT
FDEL
MMDJ DTW
Col
Physics based
2.7080
5,4990
0.0620
8,3360
79.4200
2,0220
4504
SFM
1,2590 2.1140
0,0150
622
2.5390
6.5710
0.1290
434
STGCNN
8.1608
15.8372
0.5296 5.1438
2076
7.5121
18.7721
0.5149
5.1695
1348
Data-driven
PECNet
2,0669
4,3054
0.0397 0.7431
1142
3,9674
16, 1412
0.1504
2.0986
1348
MID
8,4257
35.1797
0.3737 4,2773
1620
8.2915
47.8711
0,4384
4.7109
1076
E-V2 SC
8.8816 52.5596 7,2464
1,8844 8.8851
>9999
8,8591
60.5391 9.5011
1.1427
8,8972
>9999
PCS
L0320
1.3963
0.0126 0,4378
2.3134
6.2336
0.1070
0,9887
238
NSP
0.9884
1,4893
0.0106
0.3329
734
2,4006
6,3795
0,1199
0.9965
380
Physice-informed informed
SPDiff
1.3925
0.0092 0.3332
1.8760
4.0564
0.0671
0.7541
372
Ours
0.8861
1.3339
0.8997
0.0087
0.3269
906
1.8182
3.7292
1.8656
0.0598
0.7249
522
4 EXPERIMENTS
4.1 EXPERIMENT SETTINGS
This paper evaluates the model on two public real-world crowd datasets, GC (Yi et al., 2013) and UCY (Lerner al., 2007). These two datasets have significant differences in scene type, scale (indoor scene/outdoor scene), pedestrian density, behavior pattern, etc., which can effectively verify the generalization performance of the model in different environments. Specifically, we fol- low the experimental settings in PCS (Zhang al. 2022) and SPDifi et al., 2024): select the same 5-minute trajectory data containing rich pedestrian interactions from the GC dataset for train- ing and testing; select the same 216-second labeled trajectory data (Students003) from the UCY dataset for training and testing. We split the datasets into training and testing sets, using a training- to-testing ratio of 4: for the GC dataset and 3:1 for the UCY dataset.
Datasets.
Implementation Details. We train EnySocial-Diff using Adam with a learning rate of 1e-5 and a batch size of 32. The diffusion process uses 70 steps, and the first 25 frames of each sequence are skipped to estimate the desired walking speed. The model integrates UNet denoiser with three conditioning modules: a 3-layer GNN for Individual andvidual-Group Interaction (IGI), pretrained Re and BERT for Environmental Conditioning, and an LSTM encoder for up to 8 historical frames. All conditioning features are fused before predicting 2D accelerations. Additional architectural and computational details are provided in the Appendix.
Comparison Methods. We compare with classic physics-based and state-of-the-art data-driven and physics-informed crowd simulation methods. We choose the widely used Physics-based methods, Social Force Model (SFM) (Helbing & Molnar 1995) and Cellular Automaton (CA) (Sarmady eet al. 2010). We also compare with approaches recently published data-driven methods, including STGCNN (Mohamed et al. 2020). PECNet (Mangalam et al., 2020). MID (Gu et al. 2022). and E-V2.SC -SC (Wong al. 2024). For physics-informed comparisons, we choose PCS (Zhang et al.
2022.
NSP (Yue et al. 2022), and SPDiff (Chen et al. 2024).
Evaluation Metrics. We adopt the same evaluation settings and metries as SPDiff. At the miero level, we use mean absolute error (MAE) and dynamic time warping (DTW) to assess point-wise accuracy and temporal alignment. At the macro level, we evaluate distribution similarity using optimal transport (OT) and maximum mean discrepancy (MMD). Additionally, collision count (Col) reflects how frequently predicted trajectories enter a predefined safety radius. We also introduce the final displacement error (FDE) to capture long-term prediction stability.
Visualization results. We present both qualitative and quantitative results on the UCY dataset in Figure ( Panel (A) shows trajectory visualizations: in (a) and (b), the target pedestrian adjusts their path to avoid nearby obstacle, reflecting the importance of environmental constraints; in (c), the pedestrian moves in close synchrony with familiar individuals, highlighting the effect of pair- wise familiarity; in (d), the pedestrian aligns with the surrounding group flow while simultaneously avoiding oncoming pedestrians, demonstrating the need to model both group-level conformity and collision avoidance. Panel (B) further reports error curves (MAE and OT) across prediction hori- zons, where method consistently maintains lower errors than SPDiff, especially in long- term- predictions. These results confirm that explicitly modeling environmental cues and addvidual-grouy interactions improves both trajectory plausibility and long-horizon accuracy.
8

---

Published as a conference paper at ICLR 2026
Cumuterive VAT Time
200
150
100
Target
050
(
Pedestrian
304
200
30
480
540
60
780
Obstacle
Cumulattvv over
GT
SFM
SPDiff
Ours
Traa 206
(A)
(B)
Figure 3: Comparison with baselines on UCY. (A) Predicted trajectories: our method (cyan) follows the ground truth (blue) more closely than SFM (magenta) and SPDiff (orange). (B) Error curves over time: our method consistently achieves lower MAE and OT, especially at longer horizons.
Table 3: Ablation study structured environmental factors. The first two report results of the baseline SPDiff (Chen et al.] 2024) and its variant extended with an explicit lighting module (SPDiff+Lighting) The lower block corresponds obstacles. objects of interest (OOI), and lighting are progressively added. Results are reported on GC and UCY datasets across metrics, with checkmarks indicating the included factors.
our proposed EnvSocial-Diff" (Ours), where
Environment
GC
UCY
Model
OOI Light
MAE
OT.
FDEL
MMDJ
DTW
Col.
MAEL
OT.
FDEL,
MMDJ
DTW,
CoLl
0.9718
1.5400
0,9538
0.0100
0.3418
942
1,8833
4,2221
1,9000
0.0699
0.7496
634
x
0,9359
1.4355
0,9404
0.0099
0.3395
958
1.8395
3.8602
1.9463
0.0669
0.7357
622
U.9127
0.0087
0.3261
946
1.9488
0.7353
x
0.8990
1.3727
0.9162
0.0087
0.3253
1000
1.8337
3.8550
1.9987
0.0604
0.7259
730
0.8873
1.3455
0,9037
0.0087
0.3279
910
1.8271
3.8541
1.9671
0.0586
0.7212
648
0,8861
1.3339
0.8997
0.0087
0.3269
9016
1.8182
3.7292
1.8656
0.0598
0.7249
522
4.2 EXPERIMENT RESULTS
In Table 2 we report the results of our proposed EnvSocial-Diff' ('Ours') and comparison methods on two real-world datasets (GC and UCY). Except for E-V2-SC and Ours, which are reproduced under the same experimental settings, all other results are directly cited from SPDiff (Chen et al. 2024]. On the GC dataset, our approach achieves state-of-the tte--art performance on the MAE, OT, FDE, MMD, and DTW metrics. Since GC is indoor subscene cropped from a larger environment, with limited environmental variation and relatively simple pedestrian behaviors, existing physics- informed models (c.g., PCS, SPDiff) already fit this dataset well, leading to performance saturation. Consequently, the improvements on GC are relatively limited, yet our method still consistently out- performs all comparisons across key metrics, demonstrating its stability and applicability.
On the more challenging UCY outdoor dataset, method achieves relative improvements of 3.1 %, 8.1% 10.9%, and 3.9% on MAE, OT, MMD, and DTW, respectively, surpassing all comparison approaches and establishing metrics such as OT and MMD highlight the effectiveness of our environment factor modeling and Individual Group Interaction mechanism in capturing complex crowd dynamics and reducing long- term prediction errors in outdoor scenarios.
tfate-of-the-ar results. The substantial gains long- long-horizon
4.3 ABLATION STUDY
Ablations on Environmental Factors. The ablation study on the effectiveness of structured envi- ronmental factors is presented in Table 3 The first two rows report results of SPDiff (Chen et al.) 2024) and a variant (SPDiff-Lightin)) that we reproduced with an additional explicit lighting mod- ule. The lower block corresponds to proposed EnvSocial -Diff (Ours), where obstacles (Obs), objects of interest (OOI), and lighting (Light) are progressively added. As shown in the table, incorporating each factor consistently improves performance both GC and UCY, with the full

---

Published as a conference paper at ICLR 2026
Table 4: Ablation on the IGI module. Starting from relative motion rij. we incrementally add individual-level similarities (simly: approach tendency; siml:: motion alignment) and the group- level similarity (sim;: group conformity), Results on GC and UCY show consistent gains on most metries, with the full configuration yielding the strongest overall performance.
Model Variant
GC
UCY
sim?;
sim?,
sim?
MAEL
OT
FDEL
MMD.
DIW4
Col4
MAEL
011
FDEL
MMD1
DTW,
Colj
0.9066
1.3897
0.9192
0.0093
0,3272
990
1,0055
4.0101
2,0525
0.0628
0.7750
580
x
0.8946
1.3570
0.9194
0.0086
0.3303
1000
18886
3,8502
2.0139
0.0588
0.7720
752
0.9208
1.4137
0.9242
0.0087
0,3404
1024
1.8725
3.7937
2.0262
0.0575
0.7761
614
0.8861
1,339
0.8997
0.0087
0.3269
906
18112
3,7292
1.8656
0.0598
0.7249
522
model achieving the best results across most metries (MAE, OT, FDE, MMD, DTW). This demon- strates the effectiveness of explicitly modeling structured environment cues in crowd simulation. It is noteworthy that, the UCY dataset, adding Lighting slightly increases MMD and DTW in our model. likely due to the weaker correlation between lighting and local pedestrian dynamics in outdoor scenes. However, other key metrics (MAE, OT. FDE) continue to decrease, indicating that Lighting still contributes positively to overall prediction quality. This trend is also observed in the SPDiff baseline, where adding explicit lighting yields consistent improvements, further validating the general effectiveness of lighting as an environmental cue in trajectory prediction.
Ablations on Similarity Terms. The ablation study on the effectiveness of similarity terms in the Individual-Group Interaction (IGI) module is presented in Table 4 The first row corresponds to using only the relative motion descriptor rij. which analogous the interaction formulation in SPDiff (Chen et al.) (204)), where social forces conditioned purely relative position and ve- locity without explicit similarity measures. The following rows progressively incorporate the three similarity terms terms-siie sim,, (approach tendency). sim?, (motion aligmment), and sim? (group confor- mity). As shown in the table, adding each similarity term improves performance on both GC and UCY datasets, and the full configuration achieves the best overall results across most metrics (MAE, OT, FDE, DTW). Notably, sim; alone achieves a lower MMD on GC, but the absence of sim!, or sim?y weakens other metrics, confirming that group conformity alone is insufficient. These re- sults demonstrate that modeling complementary aspects of pedestrian interactions through explicit similarity measures leads to more accurate and socially compliant trajectory forecasts. See more experiments in the Appendix.
5 CONCLUSION
This paper presents an Env-Social Physics-Informed Crowd Simulation framework that integrates environmental
sineiing-iinluuing obstacles, objects of interest, and lighting-witt an Individ- ual-Group Interaction (IGI) module into diffusion- based Social Force models. By modeling these elements as physical forces and embedding them into learning architectures, our framework enables more realistic and context-aware trajectory predictions. Experiments demonstrate that incorporating environmental conditioning and the proposed IGI module significantly improves simulation accu- racy, particularly in complex outdoor scenes. Our approach highlights the critical role of environ- mental cues in crowd motion modeling while simultaneously achieving effective social interaction modeling. Beyond trajectory simulation, future work will extend to video-level generation based on predicted trajectories, further enhancing the framework's 'ss utility for real- world crowd simulation, safety planning, and intelligent infrastructure systems.
ACKNOWLEDGMENTS
This work was supported in parts by National Key R&D Program of China (2024YFB3908500, 2024YFB3908504). NSFC (62202312), Guangdong Basic and Applied Basic Research Foundation Shenzhen Science and Technology Program (KQTD202108 1090044003), Teaching Reform Key Program (JG2024018) 8) and Scientific Foundation for Youth Scholars and Sci- entific Development Funds from Shenzhen University.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our work focuses on modeling pedestrian dynamics for crowd simulation and trajectory prediction. The proposed EnvSocial-Din framework is designed for research and practical applications such as urban planning, public safety analysis, and intelligent transportation systems. It does not rely on per- sonally identifiable information; all datasets (GC and UCY) used in this study are publicly available and contain only anonymized pedestrian trajectories without facial biometric data. Nevertheless, we acknowledge that predictive models of human motion could potentially be misused for privacy- invasive surveillance crowd control. We encourage researchers and practitioners employ such models responsibly, respect individual privacy, and comply with relevant data protection and ethical guidelines when deploying these systems real-world scenarios.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure that our results are reproducible. All code and configuration files will be publicly available at provides detailed descriptions of the model architecture, training procedure, experimental setup, and evaluation metrics, enabling other researchers to replicate and build upon our work.
com/ zayg//nvyaaaaeeeeeeeeeeeee a al-Diff Our pa-
LLM USAGE STATEMENT
Large Language Models (LLMs), such as ChatGPT, were used to assist with language polishing, grammar correction, and improving the clarity of the manuscript. All technical ideas, model designs, experiments, and analyses conceived and executed by the authors. The LLM did not generate novel research content or influence the reported scientific results.
REFERENCES
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese. Social Istm: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 961-971, 2016.
Inhwan Bae, Junoh Lee, and Hae- Jeon. Continuous locomotive crowd behavior generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22416-22431, June 2025.
Panayiotis Charalambous, Julien Pettre, Vassilis Vassiliades, Yiorgos Chrysanthou, and Nuria Pelechano. Greil-crowds: Crowd simulation with deep reinforcement learning and examples. on Graphics, 42(4):15,200 2023.
Hongyi Chen, Jingtao Ding, Yong Li, Yue Wang, and Xiao-Ping Zhang. Social physics informed diffusion model for crowd simulation. In Proceedings of the AAAI Conference on Artificial Intel- ligence, volume 38, pp. 474-482, 2024.
Moheine Chraibi, Armel UIrich Kemloh Wagoum, Andreas Schadschneider, and Armin Seyfried. Force-based models of pedestrian dynamics. Networks Heterog. Media, 6(3):425 442, 2011.
Lan Feng, Mohammadhossein Bahari, Kaouther Messaoud Ben Amor, Éloi Zablocki, Matthiuu Cord, and Alexandre Alahi. Unitraj: unified framework for scalable vehicle trajectory pre- diction. In European Conference on Computer Vision, pp. (03.123. Springer, 2024.
Paolo Fiorini and Zvi Shiller. Motion planning in dynamic environments using velocity obstacles. The international journal of robotics research, 17(7):760-772, 1998.
Tianpei Gu, Guangyi Chen, Junlong Li, Chunze Lin, Yongming Rao, Jie Zhou, and Jiwen Lu. Stochastic trajectory prediction via motion indeterminacy diffusion. In Proceedings of the IEFF/CVF conference computer vision and pattern recognition, pp. 17113-1712, 2022.

---

Published as a conference paper at ICLR 2026
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi. Social gan: So- cially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.
2018
Xinyi Hao, Xin Zhang, Jiangtao Du, Meichen Wang, and Yalan Zhang. Pedestrians' psychological preferences for urban street lighting with different color temperatures. Frontiers in psychology, 13:971700, 2022.
Dirk Helbing and Peter Molnar. Social force model for pedestrian dynamics. Physical review E, 51 (5):4282, 1995.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851 2020.
Ling Huang, Sze Chun Wong, Mengping Zhang, ChinWang Shu, and William HK Lam. Revisit- ing hughes' dynamic continuum model for pedestrian flow and the development of an efficient solution algorithm. Transportation Research Part B: Methodological, 43(():127-141, 2009.
Roger L Hughes. A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological, 36(6)50775535, 2002.
Ryo Itatani and Nuria Pelechano. Social crowd simulation: Improving realism with social rules and gaze behavior. In Proceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games, pp. 1-11. ACM, 2024.
Sungjune Kim, Hyung-gun Chi, Hyerin Lim, Karthik Ramani, Jinkyu Kim, and Sangpil Kim. Higher-order relational reasoning for pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15251-15260, 2024.
Hoshang Kolivand, Mohd Shahrizal Sunar,
Mohd Shahriman Awang Abu Bakar, Abdulaziz Z. Fata, and Christopher Wren. An integration of enhanced social force and crowd control models for high-density crowd simulation. Neural Computing and Applications, 33(1 (6(11):6095-611) 2021. doi: 000007//00521102005533000
Jaedong Lee, Jungdam Won, and Jehee Lee. Crowd simulation by deep reinforcement learning. In ACM SIGGRAPE PH 2018 Talks. ACM, 2018. doi: 10. 144/3314745.3214777
Alon Lerner, Yiorgos Chrysanthou, and Dani Lischinski. Crowds by example. Computer graphics forum, volume 26, pp. 655-664. Wiley Online Library, 2007.
Haoyang Liang and Jie Du. A continuum model for pedestrian flow with explicit consideration of crowd force and panic effects. Transportation research part B: methodological, 100-177, 2021.
Ming Liu, Baogang Zhang, Tong Luo, Yue Liu, Boris A Portnov, Tamar Trop, Weili Jiao, Huichan Liu, Yiwei Li, and Qingyuan Liu. Evaluating street lighting quality in residential areas by com- bining remote sensing tools and survey on pedestrians' perceptions of safety and visual comfort. Remote Sensing, 14(4):826, 2022.
Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, and Adrien Gaidon. It not the journey but the destination: Endpoint conditioned trajec- tory prediction. In European conference on computer vision, pp. 759-776. Springer, 2020.
Kartrikeya Mangalam, Yang An, Harshayu Girase, and Jitendra Malik. From goals, waypoints & paths to long term human trajectory forecasting. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 15233-15242, 2021.
Huynh Manh and Gita Alaghband. Scene-Istm: A model for human trajectory prediction. arXiv
preprint arXiv: 04018, 2018.
Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel. Social-stgenn: A social pratio-temporal graph convolutional neural network for human trajectory prediction. In Proceedings of the JEEE/CVF conference on computer vision and pattern recognition, pp. 14424- 14432, 2020.
12

---

Published as a conference paper at ICLR 2026
Soraia Raupp Musse, Vinicius Jurinic Cassol, and Daniel Thalmann. A history of crowd simulation: the past, evolution, and new perspectives. The Visual Computer, 37(12):3077-3000 2021.
Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jef- frey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, et al. Scene trans- former: unified architecture for predicting multiple agent trajectories. ICLR, 2022.
Massimiliano Pascoli, Fabio Buttussi, Konstantin Schekotihin, and Luca Chittaro. Introducing agent personality in crowd simulation improves social presence and experienced realism in immersive IEEE Transactions on Visualization and Computer Graphics, 2025.
Johan Rahm and Maria Johansson. Assessing the pedestrian response to urban outdoor lighting: A full-scale laboratory study. PLoS One, 13(10):0204638, 2018.
Craig W Reynolds. Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp. 25-34, 1987.
Siamak Sarmady, Fazilah Haron, and Abdullah Zawawi Talib. Simulating crowd movements using fine grid cellular automata. In 2010 12th International Conference on Computer Modelling and Simulation, pp. 428-433 IEEE, 2010.
Jamie Snape, Jur Van Den Berg, Stephen J Guy, and Dinesh Manocha. Smooth and collision-free navigation for multiple robots under differential-drive constraints. In 2010 IEEE/RSJ interna- tional conference intelligent robots and systems, pp. 4584-4589, 2010.
Jianhua Sun, Qinhong Jiang, and Cewu Lu. Recursive social behavior graph for trajectory prediction. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pp. 660- 669, 2020.
Yunhe Tong and Nikolai WF Bode. The principles of pedestrian route choice. Journal of the Royal Society Interface, 192189):0022061, 2022.
Jur Van den Berg, Ming Lin, and Dinesh Manocha. Reciprocal velocity obstacles for real-time -ime multi-agent navigation. In 2008 IEEE International Conference on Robotics and Automation, pp- 1928-1935, 1935, 2008.
Jur Van Den Berg, Stephen J Guy, Ming Lin, and Dinesh Manocha. Reciprocal n-body collision avoidance. In Robotics Research: The 14th International Symposium ISRR, pp. 3-19. Springer, 2011.
W Warren, B Fajen, and D Belcher. Behavioral dynamics of steering, obstacle avoidance, and route selection. Journal of Vision, 133):184-184 2001.
Conghao Wong. Beihao Xia, Ziqian Zou, Yulong Wang, and Xinge You. Socialcircle: Learning the angle-based social interaction representation for pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19005-19015, 2024.
Pei Xu, Jean-Bernard Hayet, and loannis Karamouzas. Socialvae: Human trajectory prediction using timewise latents. In European Conference Computer Vision, pp. 511-528. Springer, 2022.
Shuai Yi, Hongsheng Li, and Xiaogang Wang. Understanding pedestrian behaviors from stationary crowd groups. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3488-3496, 2015.
Ye Yuan, Xinshuo Weng, Yanglan Ou, and Kris M Kitani. Agentformer: Agent-aware transform- ers for socio-temporal multi-agent forecasting. In Proceedings of the IEEF/CVF international conference on computer vision, pp. 9813-9823, 2021.
2021.
Jiangbei Yue, Dinesh Manocha, and He Wang. Human trajectory prediction via neural social physics. European conference on computer vision, pp. 376-394. Springer, 2022.
Guozhen Zhang, Zihan Yu, Depeng Jin, and Yong Li. Physics-infused machine learning for crowd simulation. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and 2022.
data mining, pp.
13

---

Published as a conference paper at ICLR 2026
A IMPLEMENTATION DETAILS
Social perception range. The social perception range is defined as the set of nearby pedestrians that are considered when modeling individual interactions. In our framework, for each pedestrian we identify their top, 6, meaning that each pedestrian interacts with their 6 closest neighbors.
Lighting features. To extract lighting features, we first convert the static scene image to the HSV color space and use the V channel to represent pixel-level brightness. The image is then divided into uniform grids, and for each grid, compute the average, maximum, and minimum light intensities. The grid size varies by dataset based on the scene's spatial scale: For the UCY dataset (720 x 576), 6 grid. For the GC dataset (1920 1060), use a grid size of 220 pixels, resulting in a 8 x 4 grid.
use a grid size of 1 10 pixels, resulting in
Model parameters. Our model has 58.2M parameters in total, including ResNet50 backbone (37.8M), a lightweight BERT encoder ( (12.6M), and diffusion module (7.9M), Among them, 42.6M parameters participate in the forward computation.
Training configurations: We use Adam (Ir= lc-5, weight decay= = lc-5) with mild StepLR decay 0799 every 10 epochs). The batch size is 32 and the diffusion process uses 70 steps. The invalid positions are masked as NaN. For each sequence, we skip the first 25 frames and compute each pedestrian's 's average velocity over these skipped frames; this value is used as the desired walking speed in the destination driving term. Training runs for 160 epochs, with each epoch taking about 69 seconds.
Model architecture: The model consists of three components. (1) Diffusion backbone: A UNet-based denoiser predicts accelerations, conditioned on Environmental Conditioning, Individ- ual-Group Interaction (IGI) and Historical Trajectories.
(2) IGI: A 3-layer GNN operates on 6-nearest neighbor graph constructed at each vaild timestep, encoding relative geometry and motion cues. (3) Environmental Conditioning: Scene information is extracted using pretrained ResNet-50 (resnet50-0676ba66) and BERT ppert-base-uncasede visual and textual embeddings are concate- nated to form obstacle, OOI, and global scene features. (4) The Historical Trajectories up past frames are encoded using an LSTM. All conditioning signals are projected and fused into the diffusion network, followed by a lightweight MLP that outputs 2D accelerations.
Computational setup: On single NVIDIA Quadro P6000 (Py Torch 1.13.1, CUDA 11.7, FP32, batch size 32, Ir=le-5, DDIM with 50 denoising steps), our full model (42.6M parameters) requires approximately 27 FLOPs per forward pass and 2.5-9.40 2.5-9.4GB of GPU memory, with each training epoch taking 69s. For 651-Frame sequence, inference took 349 seconds (=s 0.54s per frame), the allocated GPU memory ranges from 1519MB to 2047MB.
B EVALUATION METRICS
We evaluate the quality predicted trajectories using six standard metrics: Mean Absolute Error (MAE), Final Displacement Error (FDE), Optimal Transport (OT), Maximum Mean Discrepancy (MMD), Dynamic Time Warping (DTW), and Collision Count (COL). Below are their formal defi- nitions.
Mean Absolute Error (MAE). MAE computes the average &, displacement error over all predicted positions. Given predicted trajectories } and ground-truth { for N pedestrians over T time steps, the MAE is defined as:
N
T
MAE
(21)
NT
Optimal Transport (OT). OT measures the distributional discrepancy between predicted and grouhd-truth pedestrian positions using the entropy-regulatty Wasserstein distance. At each time t, the Sinkhorn distance is computed between predicted positions p (ői.... py and ground- truth p = (pi.... }:
1 T S We (pt, (p',pt).
OT = 1
(22)
14

---

Published as a conference paper at ICLR 2026
GC
Full GC
Figure 4: Comparison between the original GC subregion and the full GC scene. The left image highlights the cropped subarea (blue box) used in prior work, which limits spatial and interaction diversity. The right image shows the complete GC scene, covering broader area with higher pedes- trian density and environmental complexity, used in our extended evaluation.
where W. denotes the Sinkhorn approximation of the Wasserstein distance with regularization co- efficient
Final Displacement Error (FDE). FDE measures the error between the predicted and true positions at the final step. Let T denote the final time step, then
FDE
ST P1
(23)
N
Maximum Mean Discrepancy (MMD). MMD compares the distributions of pairwise distances among pedestrians in predicted and ground-truth trajectories. Let D and D be the intra- pedestrian distance sets at time f, then
1 MMD
T E MMD (Dfred Df
(24)
where MMD(.,) denotes the kernel-basee two-sample test using Gaussian kernels.
Dynamic Time Warping (DTW), DTW measures the similarity between two temporal sequences by computing the minimal cumulative alignment cost under temporal warping. For each pedestrian
i, DTW distance is defined as the minimum total cost path that aligns predicted trajectory { J with the ground-trute trajectory {pt ) allowing for non-linear time alignment:
DTW p.) min Σ
P. p
(25)
(f.*)
where и denotes warping path satisfying boundary, continuity, and monotonicity constraints. The final DTW metric is computed by averaging over all pedestrians:
DTW Σ (T.
(26)
Collision Count (COL). COL measures the sum number of collisions among pedestrians during prediction. A collision is counted if two pedestrians i and j within certain threshold ditres at any time
N
-
COL -
I
13111 danes).
(27)
i=it]
(=1
where T(.) is the indicator function.
15

---

Published as a conference paper at ICLR 2026
(A)
Mbrtacle
Obstaclo
SPOLIT
(C)
Ours
SPDLTF
Figure 5: Qualitative comparison between the GT, Ours, and SPDiff. (A) Near obstacles, SPDiff trajectories maybe to pass much closer to obstacles, whereas both GT and our method keep a more reasonable distance. (B) In crowded regions, SPDiff produces several near-collision interactions, while our predictions remain smoother and more socially consistent. (C)
Table 5: Comparison the full GC dataset. SPDiff uses its own social interaction module and ba- sic environment treatment limited to obstacle repulsion. In contrast, our method replaces the social interaction module with the proposed IGI design (V) and additionally integrates richer environmen- tal cues (V), including obstacles, objects of interest, and lighting. X indicates the corresponding module is not used. the table. Social and Env are marked with ** for SPDiff's built-in designs (social interaction and obstacle-only environment), / when replaced by our modules, and X when omitted.
Dataset: Full GC
Method SPDiff
Social Env MAE,
OTІ FDEJ MMD. DTW, COL
2.4624 4.6824 2.6707 0.0044 0.8873 1910
Ours /
x 2.4478
4.4662 2.6683 0.0044 0.8836 1750
/
2.3527 4.1334 2.5891 0.0039 0.8473 1710
C ADDITIONAL EXPERIMENTS
Performance Full GC scene. To further evaluate the generalizability of our method, conduct an additional experiment on the full GC scene. While the original GC benchmark restricts evaluation to manually selected subregion, manual filtering (see Figure 4p. This setting introduces greater variability pedestrian density, lay- complexity, and environmental interactions, posing significant challenge prediction models.
apply model to the entire scene without spatial cropping
16

---

Published as a conference paper at ICLR 2026
Target Podestrian
GT
SPDNT
Ours
Figure 6: Additional qualitative results for multiple target pedestrians. Each subplot visualizes one scene with selected target pedestrian (green triangle). Ground-truth future trajectory (GT) is shown in blue, while predictions from SFM (magenta), SPDiff' (orange), and our method (cyan) are overlaid. Our approach consistently aligns more closely with the GT across diverse scenarios.
Table Ablation on enhanced environmental features. We compare four settings: no enhancement (x, X), OOI-only enhancement (X, v). obstacle-only enhancement (V, X), and joint enhancement
with both (V, v.. Results on GC and UCY datasets show that the joint enhancement strategy achieves the most consistent improvements across metrics.
Env
UCY
Obe
001
MAEL
OT
FDEL
MMDJ
DTWL
Col
MAEL
OT
FDF.
MMDJ
DTW1
Coly
0,9320
1,4425
0.9391
0.0034
0.3362
926
1,9320
3.9726
2,0367
0.0609
0.7706
550
0.9038
1.3645
0.9164
0.0090
0.3333
898
191199
3.7120
2.2160
0.0606
0.7563
688
x
0,8960
1.3562
0.9160
0.0086
0.3277
962
1.8346
4.0420
1.9951
0.0634
0.7042
710
0.8861
L.3399
0.8997
0.0087
0.3269
906
1.8182
3.7292
1.8626
0.0598
0.7249
522
As shown in Table although SPDiff uses its social interaction module and obstacle-only environment treatment, replacing the interaction module with our IGI design (v) yields consistent improvements across all metrics. Furthermore, by additionally integrating richer environmental cues inniincluding obstacles, objects of interest, and lighting- -ur model achieves further gains, notably reducing OT by 11.7% (4.6824 - 1334), MMD by 11.4% (0.0044 0.0039), and COL by over 10% (1910 - 1710). These results confirm the effectiveness of our IGI-based interaction modeling and demonstrate the robustness our environment aware framework under complex real- world conditions.
Enhanced Features for Environmental Conditioning Modeling. The ablation study evaluates the impact of enhanced environmental features. We start with model without enhancement (w/o Enhance), then apply enhancement only to obstacles (Obs-Only) or only to OOI (O0I-Only), and finally apply enhancement to both (Full). As shown in Table incorporating either obstacles or OOI individually improves performance, while the Full setting achieves the best results across nearly all metrics. These findings indicate that leveraging global scene context for both obstacles and 00I provides a more comprehensive modeling of environmental effects, thereby improving trajectory prediction accuracy.
17

---

Published as a conference paper at ICLR 2026
Table 8: Ablation study of adding environmental factors the SFM method on UCY Zaral. / / X indicate whether the corresponding environmental module is enabled. The results show that incorporating obstacle, lighting, and object-of prediction performance in terms of MAE, MMD, and OT.
(OOI) cues progressively improve trajectory
Dataset: UCY Zaral
Obstacle
Lighting
001
MAE
MMD4
OT
X
2.5954
9.2648
1.6676
X
1.9585
5.2413
1.1437
X
1.8981
5.1045
1.3224
/
1.8282
4.7196
1.0968
GT
SPDiff'
Ours
Figure 7: Qualitative comparisons on the GC dataset. Each subplot shows the predicted trajectory of a target pedestrian within the marked evaluation area (blue rectangle). Ground-truth (GT) fu- ture trajectories are depicted in black, while predictions from SPDiff and our method are shown in orange and cyan, respectively. Our approach produces more accurate and socially plausible predic- tions, particularly in scenarios involving sharp turns, long-range navigation, or subtle environmental conditioning.
Table 7: Ablation study of adding environmental factors to the SFM method on UCY Students03. X indicate whether the corresponding environmental module is enabled. Progressive addition of obstacle, lighting, and object-of-interest (OOI) cues leads to consistent improvements across all evaluation metries.
Dataset:
UCY Students03
obstacle
lighting
001
MAE
MMD +
OT
COL J
X
X
x
2.8943
7.9564
0.0954
308
X
x
2.7822
7.3759
0.0899
266
V
x
2.7324
7.2187
0,0803
216
/
/
v
2.6742
6.7032
0.0708
216
SFM + Environmental Factors. To further assess the effectiveness of the proposed environmental modeling modules, we perform ablation studies based on the classic Social Force Model (SFM) by incrementally incorporating our three types of environmental factors: obstacles, lighting, and objects of interest (OOI). These experiments are conducted on the UCY dataset, focusing on two distinct scenes: Zaral and Students03.
Starting from the standard SFM as the baseline, we introduce the environmental components one by one. As shown in Tables and each added component consistently improves prediction accuracy. Obstacles reduce collisions and improve short-term precision; lighting enhances motion smoothness; and OOIs capture higher-level behavioral tendencies.
Sensitivity Analyses. addition, we conduct a series sensitivity analyses on three key hyper- parameters: the number of cloest neighbors used for individual-level similarity computation Top
18

---

Published as a conference paper at ICLR 2026
(Table 9), the number of diffusion denoising steps (Table 10). and the spatial grid size (Table [T] for environmental encoding. The results, show that the model is overall stable under variations of these hyperparameters. For Top k, values between 2 and yield comparable performance, while k == 6 provides the most balanced results across all metrics on both GC and UCY. Similarly, diffu- sion step counts from 50 to 80 exhibit only marginal fluctuations, with 70 steps offering consistent balance between accuracy and stability. Finally, the environmental grid size demonstrates moderate influence on performance, where GC performs best around resolution of 220 and UCY around 110, reflecting differences in scene scale. These observations indicate that the proposed model is not highly sensitive hyperparameter choices, and the selected default settings provide robust trade-off across datasets.
Table 9: Sensitivity analysis of the hyperparameter Top on GC and UCY datasets. Top k controls how many nearest neighbors are used when computing individual-level similarities within the IGI module. All metrics follow the lower-is-bette better convention (L). We adopt = 6 as provides the most balanced overall performance.
GC Dataset
UCY Dataset
Top_k
MAEІ
OT
FDE MMDJ DTW
CoLl
MAEL OT4
FDE] MMD DTW
Coll
0.8918
1.3409 0.9088 0.0086 0.3273 902
1,8149 3.6431 1,9209 0.0596 0.7251 588
0,8896
1.3321 0.8998 0.0087 0.3284 914
1,8142 3.7501 1.9542 0.0601 0.7007
552
6 (Ours)
0,8861
1.3339
0.8997 0.0087 0.3269 906
1.8182 3.7292 1.8656 0.0598 0.7249
522
0.8921
1.3599
0.9128 0.0089 0.3384 953
1.8154 3.7276
1.8999 0.0595 0.7159
476
Table 10: Sensitivity analysis of the diffusion step count on GC and UCY datasets. The diffusion step controls the number of denoising iterations during sampling. All metrics follow the lower-is- better convention (4). We adopt 70 steps as the default, as it provides a stable and well-balanced performance across all evaluation metries.
GC Dataset
UCY Dataset
Step
MAET
OTІ
FDEI MMD4 DTW J Col
MAEJ
OTI
FDEL MMDJ DTW
Coll
50
0,8870
1.3361
0.9019 0.0087 0.3269 984
1,8173 3.7486
1.8596 0,0616 0.7148 486
60
0.8860
1.3355 0.9006 0.0087 0.3273 912
1.8142 3.7358
1.8688 0.0603 0.7124
432
70(Ours)
0.8861
1.3339
0.8997 0.0087 0.3269 906
1,8182 3.7292
1.8656 0.0598 0.7249
522
80
0.8862
1.3449
0.8999 0.0088
0.3285 908
1.8214 3.8168
1.8705
0.0612 0.7118
582
Table 11: Sensitivity analysis of the grid size used to construct the global scene representation on GC and UCY datasets. The grid size controls the spatial resolution of the environmental encoding. with lower-is-better metrics (L). We adopt grid size of 220 for GC and 110 for UCY, as these settings yield the most balanced performance across the evaluation metrics for each dataset.
GC Dataset
UCY Dataset
Grid Size
MAEJ
OT), FDEJ MMDJ DTW.J. Col
Grid Size MAE OT FDEJ MMDJ DTWJ Col.
220rOurs)
0.8861
1.3339 0.8997 0.0087 0.3269 906
90
1.8277 3.8858 1,8794 0.0608 0.7188 546
250
0.8842 1.3308 0.8999 0.0087 0.3275
914
1.8470 3.9977 1.9471 0.0629 0.7254 596
300
0,8848
1.3393 0.8986 0.0086 0.3276
916
110(Ours)
188182 3.7292 1.8656 0.0598 0.7249 522
400
0.8840
1.3396 0.9000
0.0086 0.3276
918
120
1.8314 3.8239
1.9613
0.0600 0.7196 561
Repulsive Force. We implemented a simple repulsive-force variant: a, = a + a a rcp Σ5 cxp(-dij/ol Mij, where dij is the inter-agent distance and ni is the unit vector from j to As shown in Table [2] adding this repulsive-force term lowers collision counts but consistently worse MAE and OT on both GC and UCY, indicating that simple additive repulsion does not improve overall trajectory quality.
19

---

Published as a conference paper at ICLR 2026
Table 12: Comparison between our method and the repulsive-force variant.
Dataset
MAET
OT4
FDEI
MMD1
DTW4
COL./
GC (repulsive)
0.8970
1.3827
0.8974
0.0087
0.3370
894.0
GC (ours)
0.8861
1.3339
0.8997
0.0087
0.3269
906
UCY (repulsive)
1.8609
3,8766
1.9302
0.0646
0.7066
510.0
UCY (ours)
1.8182
3.7292
1.8656
0.0598
0.7249
522
Environment-Fiee Diffusion Variant. To assess the contribution of the environmental condition- ing within the diffusion process, we further evaluate a simplified variant in which all environmental forces are removed from the denoiser inputs and applied only afterward in a post-processing man- ner. As shown Table [3] the performance on GC changes only marginally, which is expected since GC contains stable indoor layouts with limited environmental diversity. In contrast, the gap becomes substantially larger on UCY, where open spaces and heterogeneous obstacle configurations make environmental cues more influential. These results confirm that embedding environmental in- formation directly into the diffusion dynamics is particularly important in complex, environmentally varied scenes.
Table 13: Comparison between full model and simplified variant in which the environmental conditioning removed from the diffusion inputs and applied only as forces outside the denoiser.
Dataset
MAE,
OTЛ
FDEL
MMDJ
DTWL
COLL
GC (variant)
0.8862
1.3449
0.8999
0.0088
0.3284
908.0
GC (ours)
0.8861
1.3339
0.8997
0.0087
0.3269
906
UCY (variant)
2.0764
4.4494
2.1363
0.0730
0.7854
642.0
UCY (ours)
1.8182
3.7292
1.8656
0.0598
0.7249
522
Automatic Annotations. To assess the robustness of the method to noisy automatically gener- ated annotations, we replace all manually curated boxes with raw GroundSAM detections, which contain missing and inaccurate objects. As shown in Table [4] the performance degradation on GC is limited, consistent with the fact that GC is an indoor scene with simple, well-structured geome- contrast, UCY exhibits
try that remains largely recoverable even under imperfect detections. more noticeable drop: several key elements-such as the store façade that provides strong attraction cues-are missed by GroundS AM, reducing the effectiveness of environmental conditioning. Nev- ertheless, the errors remain within reasonable range, indicating that the model retains degree of robustness to annotation noise.
Table 14: Performance comparison when replacing manually curated annotations with raw Ground- SAM detections, which introduce missing and inaccurate boxes.
Dataset
MAE!
OTІ
FDE
MMDJ
DTW]
COL
GC (auto)
0.8919
1.3440
0.9059
0.0088
0.3365
1034.0
GC (ours)
0.8861
1.3339
0.8997
0.0087
0.3269
906
UCY (auto)
1.9083
3,9220
2,0423
0.0614
0.7466
644.0
UCY (ours)
1,8182
3.7292
1.8656
0.0598
0.7249
522
ETH Dataset Generalizatio. To further assess cross-dataset generalization, we evaluate our method on the ETH dataset. As shown Table [5] our model consistently outperforms SPDiff across all metrics, demonstrating that the proposed approach generalizes well beyond the original GC and UCY datasets.
Table 15: Evaluation on the ETH dataset to assess cross- cross-dataset generalization.
Method SPDiff
MAEЛ
OTI
FDEJ
MMDJ
DTWL
COLL
0.4692
0.3153
1,7100
0.0886
0.2302
0.0
Ours
0.4083
0.2454
0.4639
0.0660
0.2162
0.0
20

---

Published as a conference paper at ICLR 2026
Density Conditioning Variant. To evaluate whether lighting can be substituted by other perceptual representations, we replace the lighting conditioning with a global density feature con- structed from a K x K density grid (K 16) and lightweight CNN encoder. As shown in Table T6 although density captures congestion levels, the substitution leads to a consistent drop across most metrics, indicating that density and lighting provide complementary rather than inter- changeable cues. At the same time, the results demonstrate that our framework can accommodate alternative perceptual inputs without requiring architectural modifications.
Table 16: Replacing lighting conditioning with a global crowd-density representation constructed from K K density grid.
Dataset
MAE
OT
FDE
MMD
DTW
COL
GC (density)
0.9132
1,4298
0.9447
0.0092
0.3407
1140
GC (ours)
0.8861
1.3339
0.8997
0.0087
0.3269
906
UCY (density)
1.8542
3.9204
1,9490
0.0606
0.7223
690
UCY (ours)
1.8182
3.7292
1.8656
0.0598
0.7249
522
D EXTRA VISUALIZATIONS
Figure presents a qualitative comparison across three representative scenarios. panel (A), tra- jectories generated by SPDiff tend pass unnaturally close obstacles, while both the ground truth and our method maintain safer margins. Panel (B) highlights behavior in dense crowds, where SPDiff produces multiple near-collision interactions, whereas our predictions remain smooth and socially coherent. Panel (C) further illustrates these differences in more complex configurations, consistently showing that our model better preserves realistic interpersonal spacing and obstacle- aware motion.
UCY Dataset. To further evaluate the effectiveness our model, we present additional qualitative results on the UCY dataset, as illustrated in Figure 6 Each subplot depicts different target pedes- trian (green triangle) across various UCY scenes, with predicted trajectories from SFM (magenta), SPDiff (orange), and our method (cyan), overlaid against the ground-truth future trajectory (blue). Across diverse motion patterns and social contexts, our approach consistently produces more ac- curate and socially plausible predictions. Our method closely follows the ground-truth trajectories, even in challenging scenarios involving group movement, sharp turns, or interactions with nearby pedestrians. Compared to existing baselines, our model better anticipates the natural flow of pedes- trian behavior and adapts more effectively to local dynamics and crowd density variations.
GC Dataset. We further demonstrate the robustness of approach through qualitative compar- isons on the GC dataset, as shown in Figure 7 Each subplot shows the predicted trajectory a target pedestrian within the blue-marked evaluation area. The ground-truth (GT) trajectory is shown in black, with predictions from SPDiff and our method rendered in orange and cyan, respectively. Our model yields trajectories that better align with the GT, especially scenarios involving long- distance navigation, abrupt direction changes, and spatial constraints along boundaries. These re- sults underscore our model's improved capacity to reason over complex environments and nuanced spatial cues.
21


Reviews:
[{"review_id": "zGzJwViWrv", "reviewer": "Reviewer_YDkj", "summary": "The paper introduces EnvSocial-Diff, a diffusion-based model for crowd simulation. Conditioned on both environmental factors such as obstacles, objects of interest, and lighting and Individual–Group Interactions (IGI), the model aims to generate realistic walking trajectories for multiple agents.\nUnlike prior approaches, EnvSocial-Diff explicitly models group conformity and leverages environmental cues beyond simple repulsive forces or binary traversability maps. Notably, it incorporates lighting conditions, which have been shown in behavioral studies to influence human motion but are largely overlooked in existing work. Trajectories are synthesized (as accelerations) using a diffusion model, conditioned jointly on the environment, IGI, and agent motion history.", "questions": "(-) in figure 1 the store is marked in dotted red but i believe it should be an object of interest which should then be marked in green?\n\n(-) how would the model handle spawning or removing of agents? I noticed that in the demo about 12 second in a pair of new nodes appears out of no where -- what is happening there? \n\n(-) the collision count in the proposed method is reduced but i would argue stil seems quite high. What is the cause of that? could re-introducing the repulsive forces help?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "(+) The proposed architecture effectively unifies and jointly models three environmental factors (obstacles, OOI, and lighting) with a social model (IGI), resulting in a powerful and more nuanced conditioning signal for the generative process, than ones used in prior works\n\n(+) the paper shows significant design effort in the Individual-Group Interaction (IGI) module, which elegantly captures social dynamics at three distinct, complementary signals: approach tendency, motion alignment and crucially group conformity.", "weaknesses": "(-) the definition of several components is lacking. This includes \nequation 1 doesn't expalin what m, v and mu are\n(-) line 237: where is this global scene feature coming from? \n(-) also the bias term as a function of the relative position between actor and obstacle isn't exaplined. why if the relative distance is larget, should the attention between actor and obstace grow?\n\n(-) the setup should be spelled out in the beginning -- what do the authors mean by scene for example -- a single BEV image with annotated objects, obstacles, their locations and type, etc. what exactly is the output -- is it for all trajectories or just a sigle \naccordingloy the dimensino of obstacle position for example should be states (2 i guess?)\n\n(-) computational cost: i didn't see a runtime analysis and suspect that since each actor requires a full denoising scheme per waypoint the number of model activations is quite large. An analysis of NFE and wall clock time would be useful. Also memory consumption. \n\n(-) the demo didn't really help me see the differences between the suggeted method and the baseline -- what are failrue cases in the baseline that are fixed or avoided byt he suggested model?\n\nminor: \n(-) the transpose op in equation 8 escaped", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Zliq8UNuwD", "reviewer": "Reviewer_ZL51", "summary": "This paper proposes EnvSocial-Diff, a diffusion-based crowd simulator that augments social-physics (SFM) with (1) structured environmental conditioning (obstacles, objects of interest, lighting, etc.) and (2) an Individual–Group Interaction (IGI) module that mixes pairwise and group-conformity signals via a GNN. On GC and UCY, the method reports consistent gains over SFM/PCS/SPDiff and other baselines across various metrics, with ablations for each factor.", "questions": "- How sensitive is the method to the accuracy and granularity of scene annotations (objects of interest, lighting, obstacle maps)? For example, if these are noisy, incomplete, or generated automatically from imperfect scene-understanding systems, how does performance degrade?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Many prior pedestrian prediction models are purely data-driven and overlook structured environmental and social factors. There are important factors in pedestrian simulation. This work tries to bridge the social-force ideas with modern generative modeling and scene modeling, which is refreshing.\n    - Accurate modelling the prior scene information will be essential for future crowd simulation works. \n\n- The decomposition into destination force + diffusion refinement is clean and easy to follow. It preserves interpretability while still benefiting from generative diversity.\n- The group similarity and alignment logic, aggregated via GNN, is conceptually simple but grounded in real social-navigation dynamics. The ablations suggest it brings in meaningful improvement.", "weaknesses": "- The **demo video** is very difficult to interpret. This is currently the biggest presentation gap. As it stands, it is hard to tell what is happening, which agents belong to which group, or how environment cues influence behavior. Since one of the main claims is improved realism and responsiveness to context, the qualitative visualization should make these effects obvious. Overlays, legends, visual callouts, and side-by-side comparisons would help a lot.\n- The framework layers several components (diffusion, semantic encoders, GNN, physics prior). While individually reasonable, it would be good to comment on compute cost and whether a simpler scene-encoder and diffusion baseline might achieve similar gains.\n- GC and UCY are standard dataset in crowd simulation, but they are relatively small scenes. Since the paper emphasizes complex social and environmental structure, a brief discussion or qualitative example in a denser or more varied environment would help support the generality claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rl7NS8NZis", "reviewer": "Reviewer_DkCA", "summary": "The paper introduces EnvSocial-Diff, a diffusion-based crowd simulation model informed by social physics and augmented with two novel modules:\n(1) a structured environmental conditioning mechanism that explicitly encodes obstacles, objects of interest (OOI), and lighting conditions, and\n(2) an Individual–Group Interaction (IGI) module that captures both fine-grained interpersonal dynamics and group-level conformity via graph neural networks.\n\nThe model extends the Social Physics Informed Diffusion Model (SPDiff, Chen et al., AAAI 2024) by incorporating richer environmental signals and multi-level social reasoning into the generative diffusion process.\nExperiments on the GC and UCY datasets demonstrate state-of-the-art performance across multiple trajectory prediction metrics (MAE, FDE, OT, MMD, DTW, and collision count). Ablation studies confirm the contributions of environmental factors and the IGI module, while qualitative visualizations support the interpretability and realism of simulated trajectories.", "questions": "> Could the authors clarify whether the environmental encoders are trained jointly with the diffusion network or frozen (especially the ResNet and BERT backbones)?\n\n> How does EnvSocial-Diff perform under dynamic environments (e.g., moving obstacles)?\n\n> Have the authors considered testing the model’s capacity for long-horizon rollouts (>5s) to evaluate accumulation of social-environmental errors?\n\n> Could the lighting conditioning be replaced or augmented by other perceptual features (e.g., crowd density maps, saliency maps)?\n\n> Is the approach compatible with large-scale agent-based simulations (e.g., thousands of pedestrians), or does the GNN limit scalability?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "+ Novel Integration: Elegant fusion of social physics and diffusion modeling with explicit environmental conditioning.\n\n+ Interpretability: Maintains physically grounded meaning for forces and accelerations.\n\n+ Comprehensive Evaluation: Multiple datasets, metrics, and ablations validate both performance and generalization.\n\n+ General Applicability: Applicable to domains such as simulation, safety planning, and digital twin environments.\n\n+ Strong Theoretical Foundation: Builds directly on the Social Force Model while extending its scope through learnable conditioning.", "weaknesses": "- Computational Complexity: The paper does not report training/inference times or resource comparisons versus SPDiff or data-driven baselines. This limits understanding of scalability in real-time simulation.\n\n- Limited Dataset Diversity: Experiments rely mainly on GC and UCY datasets. These are standard but relatively small; inclusion of additional or synthetic datasets (e.g., ETH, SDD) would strengthen generalization claims.\n\n- Lighting Factor Validation: The contribution of the lighting module is modest and potentially dataset-specific. A more detailed justification (e.g., psychophysical rationale or ablation under controlled illumination changes) would improve the argument.\n\n- Minor Clarity Issues: Some notation (e.g., the dual use of f_{\\text{light}} and \\tilde{f}_{\\text{light}}) could be clarified (e.g., f_{\\text{light}}^{\\text{raw}} and f_{\\text{light}}^{\\text{enc}}) for better readability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tiV54juoo7", "reviewer": "Reviewer_MaaY", "summary": "The paper proposes a diffusion-based crowd simulation model that jointly models structured environmental context and multi-level social interactions. EnvSocial-Diff introduces an environmental encoder that represents obstacles, points of interest, and lighting as conditioning signals for the denoising process. On the social side, an Interaction module models both interpersonal relations and group coherence using a graph-based design. These modules are fused with historical trajectories and a destination attraction term to guide generation toward socially compliant, context-aware motion. Experiments on GC and UCY show gains over state-of-the-art baselines.", "questions": "- How is the collision count defined? Does it refer to cases where predicted locations completely overlap, or is there a distance threshold?\n\n- Are collisions computed only between people, or do they also include interactions with obstacles?\n\n- For each dataset, what is the average number of people present per scene, and what are the minimum and maximum crowd sizes observed?\n\n- The authors mention that their method produces \"produce socially compliant, context-aware, and realistic trajectory predictions\". However, it is unclear how these qualities are defined or quantified. What specific criteria or metrics are used to measure social compliance or realism in the results?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 3, "strengths": "- The paper presents a thorough ablation study that isolates the contribution of each module.\n\n- The figures are clear and informative.\n\n- The authors clearly mention the motivation behind their approach and highlight how their method differs from prior work.", "weaknesses": "- The paper lacks a notation explanation section. Introducing a concise notation summary and problem setup at the start of the methods section would significantly improve clarity.\n\n- The methods section feels rushed and disorganized. Several terms and notations are introduced without being defined. The presentation lacks a logical progression, and makes it difficult to follow how each component connects to the overall framework.\n\n- No sensitivity analysis is provided for key hyperparameters, and leaves it unclear how robust the model is to tuning or parameter changes.\n\n- Implementation details are insufficient, and more information about training configurations, architectures, and computational setup would enhance reproducibility. Although the authors mention that code will be released in an anonymous repository, the link is not provided to reviewers.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)