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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video

Jae Won Jang, Yeonjin Chang, Wonsik Shin, Juhwan Cho, Nojun Kwak

We introduce 4DGS360, a di!usion-free framework for 360→dynamic object reconstruction from casual monocular video. Existingmethods often fail to reconstruct consistent 360→ geometry, as their heavyreliance on 2D-native priors causes initial points to overfit to visible sur-face in each training view. 4DGS360 addresses this challenge through anadvanced 3D-native initialization that mitigates the geometric ambigu-ity of occluded regions. Our proposed 3D tracker, AnchorTAP3D, pro-duces reinforced 3D point trajectories by leveraging confident 2D trackpoints as anchors, suppressing drift and providing reliable initializationthat preserves geometry in occluded regions. This initialization, com-bined with optimization, yields coherent 360→ 4D reconstructions. Wefurther present iPhone360, a new benchmark where test cameras areplaced up to 135→ apart from training views, enabling 360→ evaluationthat existing datasets cannot provide. Experiments show that 4DGS360achieves state-of-the-art performance on the iPhone360, iPhone, andDAVIS datasets, both qualitatively and quantitatively. Project websiteat https://jaewon040.github.io/4dgs360/

ECCV 2026

WilLaGS: Latent-Conditional 3D Appearance Fields for Robust Gaussian Splatting In-the-Wild

Yuhao Bai, Qianqiu Tan, Lilong Chen, Huanhuan Lv, Lijun Chen

3D Gaussian Splatting (3DGS) delivers real-time and high-fidelity rendering but remains challenged by unconstrained in-the-wildscenes, where drastic appearance variations and transient objects violatemulti-view consistency. Existing methods are fundamentally limited byindependent and discrete embeddings that struggle to capture continuousenvironmental changes or model spatially-varying local illumination. Toaddress these limitations, we propose WilLaGS, a unified frameworkfor robust 3D scene reconstruction and generative appearance synthe-sis under unconstrained settings. Specifically, we introduce a generativeappearance model where a β-VAE learns a structured and continuousmanifold of global appearance. Conditioned on the latent code, we con-struct a 3D neural appearance field that generates dynamic Tri-Plane fea-tures to encode spatially-varying local illumination effects. Furthermore,to suppress transient artifacts, we present a self-supervised perceptualmasking mechanism that leverages a Teacher-Student (EMA) architec-ture to derive a stable scene consensus, robustly identifying inconsistentregions via perceptual discrepancies. Extensive experiments on multipledatasets demonstrate that WilLaGS achieves state-of-the-art perfor-mance in reconstruction quality and novel view appearance synthesis,while maintaining real-time rendering efficiency.

ECCV 2026

WildSplat: Feedforward Gaussian Splatting from Unposed In-the-Wild Images

Xiyu Zhang, Jingyu Zhuang, Hongjia Zhai, Zizheng Yan, Jinwei Chen, Guofeng Zhang, Qingnan Fan

While feedforward 3D reconstruction excels at efficient novelview synthesis, it typically falters when faced with scenes under varyingillumination. To this end, we introduce WildSplat, the first feedforward3D Gaussian Splatting framework capable of appearance-conditionednovel-view synthesis for unposed in-the-wild images. To handle inconsis-tent photometric conditions, we propose a dual-branch architecture thatexplicitly decouples geometry from appearance. The geometry branchextracts an appearance-invariant 3D structure and jointly predicts cam-era poses. To govern the rendering appearance, the appearance branchinjects target appearance cues into the content features via a globallypre-modulated cross-attention mechanism. To further prevent featureentanglement, we introduce a joint multi-reference training strategy thatstabilizes the training process. Extensive experiments show that Wild-Splat surpasses existing optimization-based and feedforward methods,achieving state-of-the-art performance in in-the-wild novel view synthesisand appearance editing from sparse inputs in a single forward pass.

ECCV 2026

Why Far Looks Up: Probing Spatial Representation in Vision-Language Models

Cheolhong Min, Jaeyun Jung, Daeun Lee, Hyeonseong Jeon, Yu Su, Jonathan Tremblay, Chan Hee Song, Jaesik Park

Vision-language models (VLMs) achieve strong performanceon spatial reasoning benchmarks, yet it remains unclear whether this re-flects structured 3D understanding or reliance on statistical shortcuts innatural images. We introduce a representation-level analysis frameworkthat constructs minimal contrastive pairs to measure how spatial axes areorganized and disentangled within VLM embeddings. Our analysis acrossmultiple model families reveals a consistent vertical-distance entangle-ment: models conflate vertical image position with distance, mirroringthe perspective bias of natural photographs. This bias produces a signif-icant accuracy gap between perspective-consistent and counter-heuristicexamples, and intensifies under data scaling even as overall benchmarkaccuracy improves. We further show that models with similar benchmarkscores can exhibit different internal representations, and that these dif-ferences predict accuracy and robustness across diverse spatial reasoningbenchmarks. To isolate this bias from evaluation-set skew, we introduceSpatialTunnel, a synthetic benchmark designed to expose spatial short-cut biases by removing common correlations present in natural images.Experiments suggest that the entanglement is model-intrinsic, and thatmodels with well-separated spatial axes exhibit greater robustness, indi-cating that well-structured spatial representations lead to more reliablespatial reasoning across diverse benchmarks. Code and benchmark areavailable on the project website.

ECCV 2026

VKSR: Scalable Kernel Surface Reconstruction Using Vecchia's Approximation

Maximilian Weiherer, Chukwudi Williams Umah, Bernhard Egger

We propose Vecchia Kernel Surface Reconstruction (VKSR),an accurate implicit surface reconstruction method that efficiently scalesrecent kernel-based techniques to large point clouds with millions ofpoints. While existing (global) kernel methods work well in a sparsesetting, due to low-rank approximations, performance degrades quicklywhen presented with dense point clouds sampled from surfaces with highgeometric complexity or large-scale inputs with millions of points. Toovercome this limitation and inspired by the Gaussian Process litera-ture, VKSR uses Vecchia’s approximation instead of low-rank approx-imations, which naturally shifts computation from a global to a locallevel and allows reconstructing 14M+ points in minutes. VKSR achievesstate-of-the-art results on several challenging datasets while retainingkernel methods’ favorable properties when reconstructing sparse inputs.

ECCV 2026

Understanding the Impact of Geometric Foundation Models on Vision-Language-Action Models

Yurou Yang, Muyuan Lin, Roberto Martín-Martín, Labrie Martin, Shreekant Gayaka, Cheng-Hao Kuo, Luca Carlone

Recent work explores new opportunities at the intersectionof vision-language-action models (VLAs) and geometric foundation mod-els (GFMs) for 3D reconstruction, such as VGGT. While the resultinggeometric VLAs often show improved performance, it remains unclear(i) if modern VLAs already have sufficient geometric understanding tostart with, (ii) what is the best architecture to inject geometric under-standing into a VLA, and (iii) what is the effect of other design choicesthat affect geometric VLAs. In this paper we provide a rigorous exper-imental analysis to shed light on these questions, for a specific choiceof VLA (GR00T-N1.5) and GFM (VGGT). Our first contribution is toformalize prior work’s intuition that current VLAs lack geometric un-derstanding, by providing a rigorous analysis based on linear probing.The analysis quantifies, for the first time, the “geometric gap” betweenVLAs and GFMs. Our second contribution is to identify and comparedifferent strategies to bridge GFMs with VLAs. We implement threedifferent architectures, which differ in the way they inject geometry inthe VLA, while keeping low-level implementation details as similar aspossible, to ensure a fair comparison. Finally, we analyze the impact ofnon-architectural choices (e.g., training data, number of cameras, recon-struction quality) on the performance of the geometric VLAs.

ECCV 2026

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger

Vu Truong, Long Bao Le

Diffusion models (DMs), despite their impressive capabilitiesacross a wide range of generative tasks, have been shown to be vulnera-ble to backdoor attacks. However, existing backdoor methods face criticaltrade-offs among key factors: attack performance, stealthiness, time com-plexity, and required poison rates. For example, achieving high attackperformance typically demands a high poison rate and prolonged train-ing, which undermines stealthiness, making the attack more detectableby backdoor defenses. This paper proposes TooBad (trigger optimizationfor backdoor diffusion models), a backdoor framework which introducesa novel DM-tailored trigger optimization technique to dramatically en-hance the performance of backdoor attacks on DMs. Experiments on rep-resentative benchmarks such as CIFAR-10 show that TooBad can achievehigh ASRs (> 85%) at only 0.5% poison rate, significantly lower thanthe 10% typically required by prior work on the same datasets. At 5%poison rate, TooBad reaches nearly 100% ASR within just 3-5 backdoorinjection epochs1 , whereas existing methods need at least 30-50 epochsat double the poison rate for comparable results. Despite its potency,TooBad easily evades SOTA defenses and maintains high utility. Theseresults reveal a critical threat on DMs and highlight the need for morerobust defenses against such stealthy yet efficient attacks. Our code isavailable at https://github.com/tuanvu171/TooBad.

ECCV 2026

Tiled Prompts: Overcoming Prompt Misguidance in Image and Video Super-Resolution

Bryan Sangwoo Kim, JongHyun Park, Jong Chul Ye

Text-conditioned diffusion models have advanced image andvideo super-resolution by using prompts as semantic priors, and modernsuper-resolution pipelines typically rely on latent tiling to scale to highresolutions. In practice, a single global caption is used with the latenttiling, often causing prompt misguidance. Specifically, a coarse globalprompt often misses localized details (errors of omission) and provideslocally irrelevant guidance (errors of commission) which leads to substan-dard results at the tile level. To solve this, we propose Tiled Prompts,a unified framework for image and video super-resolution that generatesa tile-specific prompt for each latent tile and performs super-resolutionunder locally text-conditioned posteriors to resolve prompt misguidancewith minimal overhead. Our experiments on high resolution real-worldimages and videos show that tiled prompts bring consistent gains in per-ceptual quality and fidelity, while reducing hallucinations and tile-levelartifacts that can be found in global-prompt baselines. Project Page:https://bryanswkim.github.io/tiled-prompts/.

ECCV 2026

Tactile Modality Fusion for Vision-Language-Action Models

Charlotte Morissette, Amin Abyaneh, Wei-Di Chang, Anas Houssaini, David Meger, Hsiu-Chin Lin, Jonathan Tremblay, Gregory Dudek

We propose TacFiLM, a lightweight modality-fusion ap-proach that integrates visual-tactile signals into vision-language-action(VLA) models. While advances in VLAs have introduced robot policiesthat are both generalizable and semantically grounded, these modelsmainly rely on vision-based perception. Vision alone, however, cannotcapture the complex interaction dynamics that occur during contact-richmanipulation, including contact forces, surface friction, compliance, andshear. While recent attempts to integrate tactile signals into VLA modelsoften increase complexity through token concatenation or large-scale pre-training, the heavy computational demands of behaviour models necessi-tate lightweight fusion strategies. To address these challenges, TacFiLMoutlines a post-training finetuning approach that conditions intermedi-ate visual features on pretrained tactile representations using feature-wise linear modulation (FiLM). Experimental results on insertion anddrawer opening tasks demonstrate consistent improvements in successrate, direct task performance, completion time, and force stability acrossboth in-distribution and out-of-distribution tasks. Together, these resultssupport our method as an effective approach to integrating tactile sig-nals into VLA models, improving contact-rich manipulation behaviours.Project page: https://charliem7.github.io/projects/TacFilm/

ECCV 2026

Stylized Video Generation via Decoupled Data Synthesis and Gated Style Token Injection

Xin Wei, Yijie Fang, Yanjia Li, Liangyi Wu, Qin Yang, Mingrui Zhu, Nannan Wang, Xinbo Gao

Stylized video generation requires synthesizing dynamic con-tent that reflects the artistic characteristics of a reference image whilemaintaining semantic control and temporal coherence. Existing fine-tuning-based approaches suffer from two recurring limitations: motion degrada-tion caused by training on static image stylization datasets, which lacktemporal supervision and erode the temporal priors of pre-trained back-bones; and a representation bottleneck from encoder-based style injec-tion, which discards fine-grained stylistic details and bounds stylizationquality by the encoder’s representational capacity. We address both limi-tations through a unified pipeline that covers dataset construction, modelarchitecture, and training strategy. Specifically, we construct MoStyle-5K, a stylized video dataset of 5,000 videos spanning 27 artistic stylesand 4,000 preference pairs, built via a decoupled content-motion syn-thesis pipeline that explicitly preserves temporal priors in the supervi-sion signal. Building on this, we propose Implicit Gated Style To-ken Injection (IGST), which directly concatenates style patches as to-kens to bypass the encoder bottleneck, disables RoPE for style tokensto remove spatial alignment bias, and applies head-wise gated atten-tion to suppress residual structural correlations. The framework is opti-mized with a hybrid image-video objective and refined via Diffusion-DPO post-training using the structured preference pairs in MoStyle-5K. Experiments show that our method achieves superior style fidelityand motion dynamics compared with existing baselines. Project page:https://sixi111.github.io/MoStyle/.

ECCV 2026

StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description

Seung Hahm, Minh Dinh, SouYoung Jin

Long-form audio description (AD) requires more than de-scribing visible actions: it must preserve characters, events, relationships,and story context across scenes so that blind and low-vision (BLV) audi-ences can follow a film. Modern video–language models (VLMs) are effec-tive on short clips, but they often treat each moment independently, pro-ducing descriptions that miss who characters are, why events matter, andhow the current scene connects to earlier narrative context. We proposeStoryTeller, a training-free framework for story-aware long-form AD. In-stead of relying only on local visual cues, StoryTeller maintains a verifiednarrative memory that carries forward story-relevant information acrossscenes, enabling later descriptions to remain coherent, grounded, andcontextually informative. Given only raw video and a movie title, Sto-ryTeller can optionally retrieve public movie metadata to resolve namesand story context, while accepting only facts that are supported by thevideo through semantic filtering and VLM verification. The method re-quires no subtitles, scripts, AD transcripts, aligned captions, characterbanks, precomputed face identities, or task-specific fine-tuning. To evalu-ate whether generated AD preserves narrative information, we introduceStoryAD-QA1 , a question-answering benchmark that tests whether alanguage model can answer story-context questions using only the gener-ated descriptions. Experiments on standard AD benchmarks and diverselong-form videos show that StoryTeller consistently improves narrativecoherence, factual grounding, and story comprehension over strong base-lines in automatic, QA-based, and human evaluations.

ECCV 2026

SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training

Jingxing Li, Yongjae Lee, Deliang Fan

3D Gaussian Splatting (3DGS) achieves real-time novel-viewsynthesis by optimizing millions of anisotropic Gaussians, yet its trainingremains expensive, with the backward pass dominating runtime in thepost-densification refinement phase. We observe substantial update re-dundancy in this phase: many sampled views have near-plateaued lossesand provide diminishing gradient benefits, but standard training stillruns full backpropagation. We propose SkipGS with a novel view-adaptivebackward gating mechanism for efficient post-densification training. SkipGSalways performs the forward pass to update per-view loss statistics, andselectively skips backward passes when the sampled view’s loss is consis-tent with its recent per-view baseline, while enforcing a minimum back-ward budget for stable optimization. On Mip-NeRF 360, compared to3DGS, SkipGS reduces end-to-end training time by 23.1%, driven by a42.0% reduction in post-densification time, with comparable reconstruc-tion quality. Because it only changes when to backpropagate withoutmodifying the renderer, representation, or loss, SkipGS is plug-and-playand compatible with other complementary efficiency strategies, enablingadditive speedups. Code is available at https://github.com/ASU-ESIC-FAN-Lab/SkipGS.

ECCV 2026

SFM: Taming State Space Models for Text-to-Motion via Spatial-Frequency Modeling

Shang Gao, Haicheng Liao, Wenshuo Chen, Yumu Xie, Jiaxun Zhang, Bin Rao, Chengyue Wang, Yanchen Guan, Zhiyong Cui, Shiqi Ou, Yutao Yue, Zhenning Li

Text-to-motion generation aims to synthesize realistic andsemantically aligned human motions from natural language descriptions.Recent diffusion-based approaches predominantly adopt Transformer orCNN-based denoisers; However, the former suffers from quadratic com-plexity with respect to sequence length, while the latter is fundamen-tally constrained by limited receptive fields. To bridge this gap, we pro-pose SFM, a state-space-enhanced diffusion framework that introducesa structured and efficient long-range modeling scheme tailored for mo-tion generation. At its core, SFM integrates two novel components into aUNet backbone: a Frequency State-Space Module (F-SSM), which trans-forms motion features into the frequency domain with text-gated multi-scale modulation, and a Spatial State-Space Module (S-SSM), whichcaptures bidirectional spatial dependencies through parallel state tran-sitions. This dual-axis decomposition enables SFM to model both theglobal structure and fine-grained variations of human motion while pre-serving alignment with linguistic prompts. Extensive experiments onHumanML3D and KIT-ML demonstrate that SFM outperforms strongTransformer- and CNN-based baselines in both fidelity and text-motionalignment, achieving superior R-Precision and FID with optimized speed.

ECCV 2026

Rank-Aware Hyperbolic Alignment for Vision–Language Dataset Distillation

Jongoh Jeong, Sun-Kyung Lee, Kuk-Jin Yoon

Vision-language dataset distillation (VLDD) compresses alarge image-text paired dataset into a small set of synthetic pairs thatcan efficiently train contrastive vision-language models under strict dataand compute budgets. Most existing methods match expert trajectoriesor cross-modal statistics, yet still enforce full-dimensional alignment in aEuclidean embedding space. This is often overly restrictive due to rank-deficient image–text correlation, with shared semantics concentrated in alow-dimensional range and remaining variation spread across a weaklycorrelated residual subspace. LoRS relaxes alignment at the similaritylevel by low-rank factorization, but does not explicitly control domi-nant alignment capacity and structure in the representation space. Wethus propose a rank-aware hyperbolic alignment (RAHA) that combineshierarchical geometry with explicit alignment-capacity control. RAHAlifts multimodal representations to hyperbolic space and optimizes dis-tilled pairs with asymmetric objectives that enforce geodesic alignmentin the shared range while regularizing the residual subspace to preservemodality-private diversity and improve transfer robustness. Experimentson benchmarks show that RAHA demonstrates competitive cross-modalretrieval and improved transfer indicators under fixed budgets.

ECCV 2026

PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation

Peng Yun, Shouwang Huang, Hao Li, Jinxi Li, Jianan Wang, Bo Yang

Manipulating fast and dynamically moving targets in un-structured 3D environments remains challenging for embodied AI. Ex-isting visual-language-action models and world models struggle with ac-curate 3D geometry and physically meaningful forecasting. We proposePhysMani, a framework that couples a physics-principled 3D Gaussianworld model with a future-aware action policy model. The world modellearns a divergence-free Gaussian velocity field via online optimizationfor fast and physically grounded future dynamics prediction. The pol-icy model integrates the predicted 3D scene future dynamics through alearnable token based cross-attention module. We introduce PhysMani-Bench, a dynamic manipulation benchmark with 16 tasks, and demon-strate a superior success rate over strong baselines in both simulationand real-world robot experiments. Our code and data are available athttps://github.com/vLAR-group/PhysMani

ECCV 2026

Online Versatile Incremental Learning: Towards Class and Domain-Agnostic Adaptation at Any Time

Jaeho Lee, Jun-Yeong Moon, Min-Yeong Park, Jung Uk Kim, Gyeong-Moon Park

Continual learning enables vision systems to adapt to ever-changing data distributions. Despite significant advances, existing ap-proaches fail to capture continuous and concurrent shifts in classes anddomains, a critical capability for real-world deployment. This work in-troduces Online VIL (Online Versatile Incremental Learning),a novel scenario where class concepts and visual domains evolve simul-taneously online without explicit boundaries. To better adapt to thechallenges of such dynamic environments that more closely resemblereal-world conditions, we propose a novel framework TopFlow, Topologypreservation with Flow matching representation that contains two com-plementary mechanisms: Domain-agnostic Flow Matching (DFM)and Global Topology Preservation (GTP). DFM guides the modelto have domain-agnostic representations by integrating the geodesic flowkernel into contrastive learning. In contrast, GTP maintains the globalstructure of the feature space without explicitly storing past examples. Ourextensive experiments demonstrate that TopFlow e!ectively addresses thelimitations of existing methods within the Online VIL scenario, achievingstate-of-the-art performance in challenging Online VIL. The proposedmethods suggest potential directions for building continual learning sys-tems in realistic dynamic environments. Our implementation code isavailable at https://github.com/KU-VGI/Online-VIL.

ECCV 2026

NEOMAP: Novel-View Synthesis via Noise Initialization by Manifold Alternating Projection

Jinxi Li, Tianyi Zhang, Yafei YANG, Zihui Zhang, Peng Huang, Koon Lin, Bo Yang

We study the challenging problem of novel view video syn-thesis from single images or monocular videos. Existing methods, whichoperate under the assumption that pre-trained video models lack nativenovel view synthesis capability and enforce view alignment via cameraconditioning, task-specific fine-tuning, or stepwise hard denoising guid-ance, often suffer from artifacts and compromised global scene consis-tency. In this paper, we introduce NeoMap, a novel training-free frame-work designed to locate high-fidelity, view-consistent novel view solutionsfrom general pre-trained video models. The key to our approach is thecore insight that promising novel view solutions are inherently encodedwithin the natural video data manifold learned by pre-trained models,and the core challenge is simply to locate this optimal solution. We solvethis via our core mechanism: convergent manifold alternating projectioniterations that optimize the initial noise. Extensive experiments demon-strate that NeoMap significantly outperforms all existing methods across3 standard novel view synthesis benchmarks, including the challengingTanks-and-Temples, LLFF and DAVIS datasets, achieving state-of-the-art generation fidelity and top-tier view consistency. Our code and dataare available at https://github.com/vLAR-group/NeoMap

ECCV 2026

Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention

Haiqing Hao, Zhipeng Sui, Rong Zou, Zijia Dai, Nikola Zubic, Davide Scaramuzza, Wenhui Wang

Event cameras provide sequential visual data with spatialsparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural net-works exploit this low-latency advantage by updating predictions eventby event, but still suffer from two bottlenecks: recurrent architecturesare difficult to train efficiently on long sequences, and improving accu-racy often increases per-event computation and latency. Linear atten-tion is appealing because it enables parallel training and recurrent infer-ence. However, its dense state updates make per-event computation scalewith the state size, yielding a poor accuracy-efficiency trade-off for ob-ject detection, where accurate localization requires fine-grained spatialstates. The key challenge is therefore to introduce sparse state activa-tion that exploits the spatial sparsity of events while preserving efficientparallel training. We propose Spatially-Sparse Linear Attention (SSLA),which introduces a mixture-of-spaces state decomposition and a scatter-compute-gather training procedure, enabling state-level sparsity as wellas training parallelism. Building on SSLA, we develop an end-to-endasynchronous linear attention model, SSLA-Det, for low-latency event-based object detection. On Gen1 and N-Caltech101, SSLA-Det achievesstate-of-the-art accuracy among asynchronous methods, reaching 0.375mAP and 0.515 mAP, respectively, while reducing per-event computationby over 20

ECCV 2026

IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment

Huiyao Zhang, Jin Bai, Rui Guo, Jianwen Tan, Hongfei Wang, Ye Li

Prompt-conditioned foundation segmenters rely on explicit spatial prompts, such as points, boxes, or masks, to guide mask decoding. In prompt-absent deployment, however, no external/user prompts or detector-generated prompts are available at test time, creating a mismatch between the expected prompt-conditioned interface and automatic inference. We propose IP-SAM, a prompt-space adaptation framework that restores prompt-conditioned decoding without external prompts. Instead of bypassing SAM2’s prompt interface through direct feature-space adaptation, IP-SAM synthesizes complementary intrinsic foreground/background prompts and routes their dense logits through SAM2’s frozen prompt encoder, translating task-specific spatial cues into the native prompt embedding space. To reduce background leakage under severe camouflage, Prompt-Space Gating uses the intrinsic background prompt as an asymmetric suppressive constraint before decoding. Under a deterministic noexternal-prompt protocol, IP-SAM achieves state-of-the-art performance across four COD benchmarks, including MAE 0.017 on COD10K, with only 21.26M trainable parameters from SPG, PSG, a SAM2-initialized downstream mask decoder, and image-encoder LoRA, while keeping the prompt encoder frozen. Medical polyp experiments in the main paper and supplementary SOD results further show that the same prompt-space adaptation route transfers within foreground–background segmentation.

ECCV 2026

InterCMDM: Block-Causal Diffusion for Autoregressive Human Interaction Generation

Qing Yu, Kent Fujiwara

Text-conditioned human interaction generation must cap-ture both long-range temporal causality within each individual and tightlycoupled coordination between partners. Existing interaction diffusionmodels typically denoise full sequences using bidirectional attention, whichobscures causality and hinders streaming and long-horizon generation.Autoregressive alternatives enforce causality but often suffer from tem-poral drift, leading to coordination degradation and unstable interactiondynamics over time. We propose InterCMDM, a block-causal latent diffu-sion framework for autoregressive two-person interaction generation. In-terCMDM introduces a Dual-Stream Causal Diffusion Transformer thatmaintains separate causal streams for each person while modeling inter-person dependencies via unified dual-stream attention with multi-taskattention masks. These masks unify interaction modeling within a sin-gle attention mechanism and support diverse coordination behaviors, in-cluding simultaneous actions, reactive responses, leader–follower dynam-ics, and independent motion. By training a single model across thesemask configurations as a form of data augmentation, InterCMDM en-ables controllable interaction generation by simply selecting the desiredattention mask at inference time. Finally, a block-wise diffusion objec-tive enables stable latent rollout over long sequences without repeateddecode–encode cycles. InterCMDM achieves state-of-the-art performanceon InterHuman and Inter-X, improving text–motion alignment, realism,and long-horizon continuity.

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