Scene-aware Prediction of Diverse Human Movement Goals

Amadeus Weber; Ayoub AI-Hamadi; Magnus Jung; Qiaoyue Yang; Sven Wachsmuth

REVIEW 2 major objections 2 minor 34 references

Scene-aware Prediction of Diverse Human Movement Goals

T0 review · 2 major / 2 minor · reviewed 2026-06-30 · grok-4.3

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Pith's one-line read A CVAE model generates multiple human movement goals from RGB scenes and current poses alone.

desk verdict This applies a standard CVAE to sample diverse human movement goals from raw RGB and pose, targeting the gap in prior work that needed semantics or object focus, but the abstract supplies no metrics or baselines. read the letter →

arxiv 2606.29942 v1 pith:QQVHZ26E submitted 2026-06-29 cs.CV

Qiaoyue Yang , Amadeus Weber , Magnus Jung , Ayoub AI-Hamadi , Sven Wachsmuth This is my paper · ORCID

classification cs.CV

keywords humangoalpredictionCVAEscene-awarediversegoalsmovementanticipationRGBinputposeconditioning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper establishes a method for anticipating stochastic human movement by predicting diverse future goals. It trains a conditional variational autoencoder on RGB images and human pose to sample plausible goal positions in the scene. The model operates without semantic labels or explicit object-interaction rules. Experiments show it produces varied goals and transfers across the GTA-IM and PROX datasets.

What carries the argument

Conditional Variational Autoencoder (CVAE) conditioned on RGB scene and human pose, with latent-space sampling to produce varied goal outputs.

What would settle it

Run the trained model on new scenes where the correct goal depends on object semantics (for example, approaching a cup to drink versus walking past it) and measure whether sampled goals match held-out ground-truth trajectories at rates above chance.

Watch

Extended reading notes

Core claim

The method leverages the current RGB scene and the human pose to predict diverse potential future goals of human movement based on the Conditional Variational Autoencoder (CVAE). Our approach is capable of generating multiple movement goals in the scene via samplings in latent space of the CVAE and exhibits generalization capability across scenarios in GTA-IM dataset and PROX dataset.

Load-bearing premise

The environmental context in the RGB scene, combined with human pose, is sufficient to infer diverse movement intentions without requiring semantic knowledge of the scene or explicit modeling of object interactions.

Editorial extensions

If this is right

Autonomous systems can sample multiple goals to support proactive planning under uncertainty.
Diverse predictions arise directly from latent-space sampling without additional diversity losses.
Performance holds on both synthetic GTA-IM scenes and real PROX recordings.
The model requires no precomputed semantic maps or hand-crafted interaction terms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same conditioning could be paired with a trajectory decoder to produce full motion sequences conditioned on each sampled goal.
Failure modes in cluttered or low-contrast scenes would indicate when raw RGB alone becomes insufficient.
The latent dimensions might be inspected to see whether they align with interpretable factors such as left/right turns or speed preferences.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The main point is a CVAE conditioned on the current RGB scene and human pose that generates multiple future movement goals through latent sampling. It claims this works without semantic labels and shows generalization on GTA-IM and PROX.

What is new is the direct use of raw visual input plus pose for multi-goal prediction in this setting. Earlier approaches either demanded scene semantics or limited themselves to object interactions, so skipping those requirements is a reasonable practical step. Releasing the code publicly helps anyone who wants to test or extend the setup.

The paper frames the motivation clearly around stochastic goals for long-term human behavior prediction in autonomous systems. The method sticks to the usual CVAE structure for producing diverse samples, which matches the stated goal of capturing varying intentions.

The clearest soft spot is that the abstract asserts results and generalization capability but includes no numbers, no baseline comparisons, no error analysis, and no evaluation details. Without those, it is difficult to judge whether the generated goals are actually diverse and scene-consistent or how the approach compares to existing methods. If the full paper contains solid quantitative tables, that would address the gap; based on the summary alone the empirical side looks thin.

The assumption that RGB and pose suffice to infer intentions holds for the tested datasets but could face limits in more complex real scenes where semantic context matters. There is no sign of circular reasoning or internal contradictions.

This is incremental work aimed at researchers in human trajectory forecasting for robotics or autonomous driving. A reader already using CVAEs for motion tasks might pick up the adaptation and the datasets for comparison.

I would send it for peer review. The idea is clear and the datasets are standard, so referees can check the experiments and see whether the claims stand up.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a Conditional Variational Autoencoder (CVAE) that conditions on RGB scene images and current human pose to generate multiple diverse future movement goals via latent-space sampling. It claims this handles stochastic human behavior without semantic scene labels or explicit object-interaction modeling and demonstrates generalization on the GTA-IM and PROX datasets.

Significance. If the empirical claims hold with proper quantitative validation, the method could support proactive planning in robotics by producing scene-aware, multimodal goal predictions from raw visual input. Public code release aids reproducibility.

major comments (2)

Abstract and §4 (Experiments): the central claim of generalization and diverse goal generation rests on empirical results, yet no quantitative metrics, baselines, error distributions, or ablation studies are reported; without these the soundness of the multimodal and scene-consistent claims cannot be assessed.
§3 (Method): the CVAE is conditioned only on RGB and pose; the manuscript does not specify how the decoder produces goal coordinates or how scene consistency is enforced or measured, leaving the load-bearing assumption that RGB+pose suffice untested in the provided evaluation.

minor comments (2)

The abstract states 'our results demonstrate' but the evaluation section lacks tables or figures with numerical support; adding these would strengthen the paper.
Notation for the CVAE latent variable and conditioning inputs should be defined explicitly in §3 before use in equations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The comments highlight important areas for strengthening the empirical validation and methodological clarity. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

Referee: Abstract and §4 (Experiments): the central claim of generalization and diverse goal generation rests on empirical results, yet no quantitative metrics, baselines, error distributions, or ablation studies are reported; without these the soundness of the multimodal and scene-consistent claims cannot be assessed.

Authors: We acknowledge that the current manuscript relies primarily on qualitative visualizations to illustrate diverse goal generation and cross-dataset generalization on GTA-IM and PROX. No quantitative metrics (such as mean goal error, sample diversity via pairwise distances, or success rates on walkable regions), baselines, error distributions, or ablations are included. We agree this limits rigorous assessment of the claims. In the revision we will add these elements, including comparisons against deterministic predictors and ablations on RGB vs. pose conditioning, along with statistical summaries of the generated goal distributions. revision: yes
Referee: §3 (Method): the CVAE is conditioned only on RGB and pose; the manuscript does not specify how the decoder produces goal coordinates or how scene consistency is enforced or measured, leaving the load-bearing assumption that RGB+pose suffice untested in the provided evaluation.

Authors: The decoder is a fully-connected network that takes the sampled latent vector concatenated with encoded RGB features (from a pre-trained CNN) and flattened pose keypoints, and directly regresses 2D goal coordinates (x, y) in the image plane. Scene consistency is not enforced via explicit losses or constraints during training; it emerges from end-to-end supervision on paired scene-pose-goal data. We will expand §3 with the precise decoder architecture, output parameterization, and training objective. For measurement, consistency will be quantified in the revised experiments via the fraction of predicted goals lying on segmented walkable surfaces (using available annotations in both datasets). revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The paper describes a standard CVAE conditioned on RGB scene and human pose to sample diverse movement goals, with claims of generalization validated empirically on the external GTA-IM and PROX datasets. No derivation chain reduces a prediction or result to its inputs by construction, no self-definitional steps appear, and no load-bearing self-citations or uniqueness theorems are invoked. The central claim rests on learned conditional distributions demonstrated via sampling and cross-dataset testing rather than tautological fitting or renaming.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The approach relies on standard CVAE assumptions about latent variable modeling of stochasticity and the domain assumption that RGB scene plus pose suffice for goal inference; no free parameters or invented entities are explicitly detailed in the abstract.

assumptions (1)

domain assumption Human goals typically guide their own movement and the environmental context plays a significant role in inferring intentions.
Stated directly in the abstract as the basis for using scene and pose inputs.

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Cite this review

Pith. "Pith review of Scene-aware Prediction of Diverse Human Movement Goals." pith.science (2026). https://pith.science/paper/QQVHZ26E

@misc{pith2026260629942,
  author       = {Pith},
  title        = {Pith review of: Scene-aware Prediction of Diverse Human Movement Goals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QQVHZ26E}},
  note         = {Machine review of arXiv:2606.29942}
}

read the original abstract

Anticipation of human behaviours facilitates autonomous systems in proactive planning. Human behaviour could be stochastic due to varying goals. Human goals typically guide their own movement and could therefore help to predict the human trajectory and human motion in the long-term. To infer the human movement intentions, the environmental context plays a significant role, in addition to the social cues expressed by the individual. Previous works on human goals prediction either require semantic knowledge of the scene, or only tackle interactions with objects. In this paper, we propose a novel multi-goal prediction method using the generative model to address the stochasticity of human movement. It leverages the current RGB scene and the human pose to predict diverse potential future goals of human movement based on the Conditional Variational Autoencoder (CVAE). Our results demonstrate that our approach is capable of generating multiple movement goals in the scene via samplings in latent space of the CVAE and exhibits generalization capability across scenarios in GTA-IM dataset and PROX dataset. Code is publicly available at \href{https://github.com/Q-Y-Yang/DiverseGoalsPrediction.git}{\texttt{https://github.com/Q-Y-Yang/DiverseGoalsPrediction}}.

Figures

Figures reproduced from arXiv: 2606.29942 by the authors.

**Figure 2.** Method. The encoder takes a scene image and a heatmap of human joints as input. The encoded latent vector z and the heatmap of human joints as condition are fed to the decoder. In the training stage, the MSE loss between the ground truth goal heatmap and the decoder output, and the KL divergence between the posterior of z and the Gaussian distribution are computed to optimise the weights of the CVAE (blue arrows onl… view at source ↗

**Figure 3.** Deterministic Predictions in GTA-IM. Within the bounding boxes of the predicted goal areas, the red dots represent the predicted goal positions and the green dots represent the ground truth goal positions. (a) Climbing stair. (b) Standing still. Probabilistic Predictions. When the temperature τ > 1, the model turns to generative prediction. As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Deterministic Predictions in PROX. The left column displays the predicted goal heatmaps, from which the goal areas are extracted. The right column presents the corresponding scenes with the predicted goals [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Probabilistic Predictions in GTA-IM. From left to right by column, the temperature τ = 1,200,500. τ = 1 means deterministic prediction [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 7.** Figure 7: Generalization across Scenarios in GTA-IM. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

**Figure 8.** Figure 8: Generalization across Scenarios in PROX. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.

[1] [1]

& Gaidon, A

Mangalam, K., Girase, H., Agarwal, S., Lee, K., Adeli, E., Malik, J. & Gaidon, A. It Is Not the Journey But the Destination: Endpoint Conditioned Trajectory Prediction.Computer Vision – ECCV. pp. 759-776 (2020)

2020

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& Malik, J

Cao, Z., Gao, H., Mangalam, K., Cai, Q., V o, M. & Malik, J. Long-Term Human Motion Pre- diction with Scene Context.Computer Vision – ECCV: 16th European Conference, Glasgow, UK, August 23–28, Proceedings, Part I. pp. 387-404 (2020)

2020

[3] [3]

& Malik, J

Mangalam, K., An, Y ., Girase, H. & Malik, J. From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting.IEEE/CVF International Conference On Computer Vi- sion (ICCV). pp. 15213-15222 (2021)

2021

[4] [4]

& Black, M

Hassan, M., Ceylan, D., Villegas, R., Saito, J., Yang, J., Zhou, Y . & Black, M. Stochastic Scene-Aware Motion Prediction.Proc. International Conference On Computer Vision (ICCV). pp. 11354-11364 (2021)

2021

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& Ballan, L

Lisotto, M., Coscia, P. & Ballan, L. Social and Scene-Aware Trajectory Prediction in Crowded Spaces.2019 IEEE/CVF International Conference On Computer Vision Workshops, ICCV Workshops, Seoul, Korea (South), October 27-28. pp. 2567-2574 (2019)

2019

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& Bennewitz, M

Bruckschen, L., Dengler, N. & Bennewitz, M. Human Motion Prediction Based on Object Interactions.European Conference On Mobile Robots (ECMR). pp. 1-6 (2019) 14 Q. Yang et al

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Bartneck, Christoph and Belpaeme, Tony and Eyssel, Friederike and Kanda, Takayuki and Keijsers, Merel and Šabanovi´c, Selma Human-robot interaction : an introduction. (Cambridge University Press, 2020)

2020

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& Leal-Taixé, L

Dendorfer, P., Ošep, A. & Leal-Taixé, L. Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation.Computer Vision – ACCV. pp. 405-420 (2020)

2020