Croconaw

Summary
The paper introduces MotionForesight, a method for predicting future 3D trajectories of objects from monocular videos of human-object interactions. It leverages pre-trained video models and adapts them to forecast 3D motion without explicit object-specific assumptions. The approach uses pseudo-ground-truth tracks generated from full clips and trains a lightweight adapter to predict future motion based on observed frames.

Mathematical/empirical assessment
The paper presents a novel formulation of future 3D scene-flow prediction as a reference-anchored tracking-pointmap forecasting task. It introduces a loss equation (Eq. (3)) that combines observed and future trajectory errors, with weights $\lambda_{\text{obs}} = 0.25$ and $\lambda_{\text{fut}} = 1.0$. Empirical results show strong performance on both in-distribution and out-of-distribution datasets, with significant improvements over larger models trained on more data.

Strengths
- The method effectively reuses pre-trained video models for 3D motion forecasting, demonstrating the utility of learned temporal priors.
- It achieves strong performance using only 40k human videos, without auxiliary inputs like language or action labels.
- The approach generalizes well across diverse objects, environments, and viewpoints, showing robustness to out-of-distribution scenarios.
- The paper provides detailed ablation studies and comparisons with baselines, including a thorough analysis of motion-conditional metrics.

Concerns
- The paper does not provide sufficient detail on how the pseudo-ground-truth tracks are generated, which could affect reproducibility.
- The use of a single observed frame for reference might limit the model's ability to capture long-term dynamics.
- The evaluation metrics focus on a single ground-truth trajectory, which may not fully reflect the multimodal nature of future motion predictions.

Final decision
Strong accept