arXiv Computer Vision
Event-Only Wingbeat Counting under Camera Motion: A Controlled MuJoCo Benchmark
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arXiv Computer Vision
Sep 16
Optical-Flow Wingbeat Counting in MuJoCo: A Comparison of Convolutional, Spiking, and Attention-Based Temporal Models
The paper evaluates optical‑flow based wingbeat counting on simulated Crazyflie vehicles using MuJoCo. Three temporal models—causal temporal convolution, leaky integrate‑and‑fire spiking, and causal self‑attention—were combined with a shared convolutional encoder and tested on 1,440 clips at 1.5 m and 3.0 m distances. Exact‑count accuracies ranged from 92.22 % to 96.67 %, with no significant differences between models, and the study highlights the need to report both total counts and event‑level timing.
arXiv Computer Vision
6d ago
DAPEVO: Deep Adaptive Patch Frame-Event Visual Odometry
DAPEVO is a learned visual odometry system that independently estimates image and event correspondences at shared patch locations and fuses their correlation evidence before motion refinement. It maintains image and event descriptors for each tracked patch, using a learned scalar gate to combine modality-specific correlation embeddings for each patch–frame edge, followed by a shared recurrent refinement and bundle‑adjustment update. The method supports event‑only observations and modality‑aware keyframe culling, achieving low trajectory error even when RGB frames are sparse or degraded, outperforming DPVO, RAMP‑VO, and event‑only DEVO on UZH‑FPV and TartanEvent datasets.
arXiv AI
Sep 16
PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control
arXiv:2609.17521v1 Announce Type: cross Abstract: Interactive control for video generation is moving from coarse prompts toward fine-grained, physically meaningful manipulation of dynamic scenes. Yet...
arXiv AI
Sep 15
LPA-CWM: A Learned Physical Adjudicator for Motion Reasoning with Counterfactual World Models
LPA-CWM introduces a Learned Physical Adjudicator (LPA) to improve counterfactual world models (CWM) for motion reasoning by learning to weight candidate responses based on visual context and response structure. The 3.0M‑parameter LPA is trained on dense MOVi‑F trajectories while keeping the CWM predictor and intervention generator frozen. A new Completeness‑aware Motion Correspondence (CMC) protocol evaluates localization, trajectory completeness, visibility, and continuity, and LPA‑CWM achieves significant gains on DAVIS and Kinetics subsets.
arXiv Computer Vision
Sep 21
Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering
arXiv:2504.15776v2 Announce Type: replace Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccur...
arXiv AI
Sep 15
Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly
arXiv:2609.07534v2 Announce Type: replace-cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sus...