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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

Table-MCR2TR: Merged-Cell-Aware Table Recognition via Reinforced Multimodal Language Models

Bangbang Zhou, Zhaoqing Zhu, Feiyu Gao, Hangdi Xing, Yadong Qu, Qi Zheng, Ming Yan, Hongtao Xie

Table recognition (TR) aims to transcribe table images into semi-structured representations (e.g., HTML or Markdown). However, current methods still struggle with complex structures, particularly involving merged cells, which are essential for accurate table parsing and downstream understanding tasks. While some works improve TR through large-scale training and global supervision, these local fine-grained yet crucial merged-cell attributes are often overlooked, becoming a key bottleneck for further progress. To tackle this, we introduce Table-MCR2TR, a reinforced multimodal large language model framework that leverages the enhanced merged-cell recognition (MCR) ability as contextual guidance to improve table recognition quality. To enable this capability, we first develop a high-fidelity data pipeline to construct a large-scale table dataset covering many complex merged cells, alleviating their scarcity in existing datasets. Additionally, the merged-cell-aware table recognition (MCATR) optimization strategy is proposed to improve table parsing via collaborative reinforcement learning. The core of MCATR lies in integrating three complementary objectives: TR and MCR respectively learn the global table structure and fine-grained merged-cell information; based on these learned signals, the MCR2TR alignment task transfers merged-cell knowledge to guide TR generation. Experimental results show that Table-MCR2TR achieves SOTA across multiple benchmarks, with average TR/MCR accuracy of 92.3%/83.9%, outperforming top-tier models and further boosting downstream table QA performance.

ECCV 2026

Kilometer-Vision: A New Frontier for Large-Scale Spatial Awareness in VLMs

Aravindh Mahendran, Michael King, Matthew Grimes, Antoine Yang, Tyler Zhu, Joseph Heyward, Tengda Han, Shiry Ginosar, Chen Sun, Dima Damen, Simon Osindero, Noah Snavely, Simon Lynen, Joao Carreira, Viorica Patraucean

We push the frontier of large-scale spatial intelligence in Vision-Language Models (VLMs) and introduce the first benchmark that probes geographical layout understanding from real-world videos, spanning up to 1km distances. Inspired by the cognitive science literature, we evaluate models against the hierarchical stages of human spatial awareness: anchoring via landmarks, connecting them through routes, and integrating these into global mental maps. Extensive experiments reveal a fundamental divergence in how current AI models process spatial information. Instead of utilising true path integration or forming geometric survey knowledge, we find that VLMs rely almost entirely on 2D visual recognition and text-matching to bypass complex spatial reasoning. The benchmark is publicly available at https://perception-testchallenge.github.io/kilometervision.html.

ECCV 2026

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification

Yinghao Jin, Xi Yang

Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. However, in fine-grained image classification, assessing this informativeness reliably is especially challenging due to subtle differences between classes. In this paper, we introduce a novel active learning method, combining DiscrEpancy-Confusion uncErtainty and calibRatioN diversity for active fine-grained image classification (DECERN), to effectively perceive the distinctiveness between fine-grained images and evaluate the sample value. DECERN introduces a multifaceted informativeness measure that combines discrepancyconfusion uncertainty and calibration diversity. The discrepancy-confusion uncertainty quantifies the structural stability and category directionality of fine-grained unlabeled data during local feature fusion. Subsequently, uncertainty-weighted clustering is performed to diversify the uncertainty samples. Then we calibrate the diversity to maximize the global diversity of the selected sample while maintaining its local representativeness. Extensive experiments conducted on 7 fine-grained image datasets across 39 distinct experimental settings demonstrate that our method achieves superior performance compared to state-of-the-art methods.

ECCV 2026

A Benchmark and Multi-Agent System for Instruction-driven Cinematic Video Compilation

Peixuan Zhang, Chang Zhou, Ziyuan Zhang, Hualuo Liu, Chunjie Zhang, Jingqi Liu, Xiaohui Zhou, Xi Chen, Shuchen Weng, Si Li, Boxin Shi

The surging demand for adapting long-form cinematic content into short videos has motivated the need for versatile automatic video compilation systems. However, existing compilation methods are limited to predefined tasks, and the community lacks a comprehensive benchmark to evaluate the cinematic compilation. To address this, we introduce CineBench, the first benchmark for instruction-driven cinematic video compilation, featuring diverse user instructions and highquality ground-truth compilations annotated by professional editors. To overcome contextual collapse and temporal fragmentation, we present CineAgents, a multi-agent system that reformulates cinematic video compilation into “design-and-compose” paradigm. CineAgents performs script reverse-engineering to construct a hierarchical narrative memory to provide multi-level context and employs an iterative narrative planning process that refines a creative blueprint into a final compiled script. Extensive experiments demonstrate that CineAgents significantly outperforms existing methods, generating compilations with superior narrative coherence and logical coherence.

ECCV 2026

Adaptive Noise Covariance Scheduling under Riemannian Metrics for Diffusion Models

Bolin Deng, Die Hu, Bin Tan, Jun Wu

Diffusion models typically reconstruct coarse, low-frequency structure before recovering high-frequency detail during the reverse denoising. However, the standard forward process injects Gaussian white noise that corrupts all frequencies uniformly, creating a mismatch between forward corruption and reverse denoising and hindering the recovery of fine detail. We address this mismatch with an adaptive noise covariance schedule that evolves along the geodesic on the Symmetric Positive Definite (SPD) manifold under the Bures-Wasserstein (BW) metric. Specifically, we evolve the covariance along the BW geodesic from a blue-noise endpoint to a white-noise endpoint, so the noise power spectrum transitions smoothly from high-frequency emphasis to uniform. This transition better aligns forward corruption with reverse denoising and enhances textural detail. We provide theoretical and quantitative analyses showing that this transition path yields smoother noise power spectrum evolution and lower noise power fluctuations. In addition, we adapt the transition speed to the characteristics of each image, yielding finer forward-reverse alignment and improved perceptual quality. Experiments on multiple datasets show consistent gains over deterministic diffusion baselines in FID and KID. Our code and pretrained models are available at https://github.com/BolinDeng/RMDM.

ECCV 2026

Real-Time Source-Free Object Detection

Sairam V C Rebbapragada, Varun Gopal, Poornima Jain, Vineeth N Balasubramanian, Muhammad Haris Khan

Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone. We show this trade-off is unnecessary: building on YOLOv10, an NMSfree dual-head detector, we achieve state-of-the-art adaptation accuracy while being faster and more compact. We observe that directly applying vanilla mean-teacher self-training to dual-head detectors leads to suboptimal adaptation performance due to two key factors. First, simple pseudo-label generation strategies, such as using a single head or directly combining high-confidence predictions from both heads, yield suboptimal supervision under domain-shift. We propose DHF (Dual-Head Pseudo-Label Fusion) which selectively admits one-to-one (O2O) and one-to-many (O2M) head predictions, preserving precision and recovering missed objects. Second, we observe domain-shift collapses multi-scale feature discriminability. We propose the use of our MARD (Multi-scale Adaptive Representation Diversification) loss which mitigates this by enforcing detection-aware variance and covariance constraints on multiscale feature maps. Both modules are training-time only, leaving inference unchanged. Across domain-shift benchmarks, our method, RTSFOD yields 1.4 to 3.5% mAP gains, 1.3× higher throughput, with ∼2× fewer parameters than prior state-of-the-art SFOD methods, thus advancing the Pareto frontier of the speed-accuracy-model size trade-off. We report main results with YOLOv10, and demonstrate generalizability with additional YOLOand DETR-based dual-head detectors. Code is available here: https://github.com/Sairam13001/RT-SFOD/.

ECCV 2026

Distribution-Alignment Bridge for Uncertainty-Aware Text-to-Video Retrieval

Kyeongmo Chae, Jihoon Lee, Sangtae Ahn

This paper proposes the Distribution-Alignment Bridge (DAB), a framework that reconceptualizes text-to-video retrieval as a distribution alignment task rather than traditional deterministic point matching. By modeling both text and video embeddings as Gaussian distributions defined by mean and variance, DAB explicitly accounts for modalityspecific uncertainty. We employ a deterministic, diffusion-inspired bridge to iteratively refine text distributions toward their target video distributions through a truncated refinement process. This approach unifies probabilistic embedding and distributional transformation into a cohesive, end-to-end trainable system. To optimize cross-modal similarity, we introduce a distribution-aware contrastive loss based on Kullback–Leibler divergence. Extensive evaluations on MSR-VTT, MSVD, and VATEX benchmarks confirm that DAB significantly outperforms existing probabilistic and diffusion-based baselines, while providing calibrated uncertainty-aware ranking through bridge-induced distributional margins.

ECCV 2026

MMDiff: Extending Diffusion Transformers for Multi-Modal Generation

Yagmur Akarken, Orest Kupyn, Christian Rupprecht

Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising trajectory are discarded once the content is rendered. We present MMDiff, a framework that transforms a frozen diffusion transformer into a multi-modal generative system that jointly produces images alongside any combination of dense perceptual modalities using lightweight decoder heads. Our central finding is that perceptual information is temporally distributed along the denoising trajectory, and that multi-timestep feature fusion with spatially varying aggregation weights is essential, improving semantic segmentation results by up to 28.7% mIoU over single-timestep extraction. We further adopt conceptdriven attention extraction for interpretable spatial guidance, and show that frozen diffusion features are competitive with and complementary to state-of-the-art encoders such as DINOv3. By training only lightweight decoder heads on a frozen backbone, we achieve strong performance in semantic segmentation, salient object detection, and depth estimation, and demonstrate that this framework enables effective synthetic data generation at scale. Code, models, and results are available here.

ECCV 2026

SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation

Jiongze Yu, Xiangbo Gao, Pooja Verlani, Akshay Gadde, Yilin Wang, Balu Adsumilli, Zhengzhong Tu

Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal. Specifically, users can first super-resolve or optionally a small set of keyframes using any off-the-shelf image superresolution (ISR) model, then SparkVSR propagates the keyframe priors to the entire video sequence while remaining grounded by the original LR video motion. Concretely, we introduce a keyframe-conditioned latent-pixel two-stage training pipeline that fuses LR video latents with sparsely encoded HR keyframe latents to learn robust cross-space propagation and refine perceptual details. At inference time, SparkVSR supports flexible keyframe selection and a reference-free guidance mechanism that continuously balances keyframe adherence and blind restoration, ensuring robust performance even when reference keyframes are absent or imperfect. Experiments on multiple VSR benchmarks demonstrate improved temporal consistency and strong restoration quality, surpassing baselines by up to 24.6%, 21.8%, and 5.6% on CLIP-IQA, DOVER, and MUSIQ, respectively, enabling controllable, keyframe-driven video super-resolution. Moreover, we demonstrate that SparkVSR is a generic interactive, keyframe-conditioned video processing framework as it can be applied out of the box to unseen tasks such as old-film restoration and video style transfer. Code is available at: https://github.com/tacogroup/SparkVSR.

ECCV 2026

Synesthesia via Direct Latent Augmentation: Bypassing the Decode-Encode Loop for Cross-Modal Distillation

Cristian Sbrolli, Nicolas Michel, Matteo Matteucci, Toshihiko Yamasaki

While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfectly paired datasets. Recent methods address this data bottleneck by synthesizing missing modalities via generative AI, yet they introduce a severe inefficiency: the Decode-Encode Loop. Specifically, information-rich generative latents are decoded into noisy raw signals, forcing the downstream classifier to waste capacity re-encoding them. To bypass this bottleneck, we propose Direct Latent Augmentation (DLA), utilizing undecoded generative latents directly as privileged information. Furthermore, to transfer this dense knowledge to a purely visual student, we introduce Multilayer Explicit Simulated Synesthesia (MESSy). Instead of enforcing rigid representation matching, which forces the student to distort its native visual features to accommodate complex multimodal topologies, MESSy uses a predictive objective to safely internalize these physical priors. Empirical results demonstrate that our framework significantly outperforms raw data augmentation and traditional distillation. Ultimately, our approach yields highly accurate unimodal students with “synesthetic” latent structures that are inherently aligned with modalities they have never directly observed.

ECCV 2026

Enhanced Neural Video Representation Compression with High Scalability

Ho Man Kwan, Tianhao Peng, Fan Zhang, Mike Nilsson, Andrew Gower, David Bull

Implicit neural representations (INRs) have recently emerged as a promising approach to video compression, delivering competitive ratedistortion performance alongside rapid decoding. However, existing neural video codecs struggle to balance complexity and scalability. Lightweight models often suffer from degraded compression performance when scaled to different bitrate/quality levels, whereas high-performance models exhibit limited scalability, as their model complexity typically increases with quality. This lack of a unified architecture capable of maintaining consistent complexity across a wide range of bitrates severely limits their diverse real-world deployment. To address these challenges, we introduce NVRC++1, a novel INR-based video codec that utilizes a lightweight INR with multiple high-resolution feature grids, providing high scalability at any given complexity level. This is paired with an optimization framework that enables efficient overfitting on high-resolution grids for long video sequences, thereby exploiting spatio-temporal redundancies without prohibitive computational or memory overhead. Additionally, an advanced entropy model is designed for efficiently compressing the high-dimensional grid parameters. As a result, NVRC++ provides four complexity levels (from 7kMACs/pixel to 360kMACs/pixel), each spanning wide bitrate and quality ranges while supporting real-time decoding. The experimental results show that NVRC++ offers a much faster decoding speed (up to 7.6x) compared to the SOTA INR-based video codec, NVRC, while delivering comparable performance.

ECCV 2026

SPHERE: From MRI Sampling Mechanisms to Spatial Priors for Generalizable Brain Tumor Segmentation

Jiacheng Lu, Shiyu Zhang, Hui Ding, junhui xin, Guoping Huo

Brain tumor MRI segmentation is essential for clinical diagnosis and treatment planning. Although deep learning methods have achieved significant progress, most models operate solely in the image domain and overlook the influence of k-space sampling on spatial structure formation. This physical information gap often limits model generalization when encountering varying scanning conditions across different clinical centers. To address this issue, we propose SPHERE, Sampling-Prior Harmonized rEconstruction-aware Representation lEarning, a samplingaware framework for brain tumor MRI segmentation. SPHERE-Recover estimates k-space sampling–related geometric parameters directly from reconstructed MRI and transforms discrete scanning conditions into a continuous deformation vector field (DVF) that represents samplinginduced structural perturbations. Building on this prior, we develop SPHERE-Seg and introduce a Deformation Prior Module (DPM) to inject the DVF into feature modeling, enabling sampling-driven structural consistency learning. Experiments on BraTS and MSD show that SPHERE improves segmentation accuracy, boundary stability, and crossdataset generalization. These results demonstrate that explicitly modeling MRI sampling mechanisms yields robust, generalizable, and efficient brain tumor segmentation.

ECCV 2026

Cross-token Guidance Transformer for Weakly Supervised Object Localization

Zhiwei Chen, Yiran Nie, Ruize Han, Qinqin Zhou

Weakly supervised object localization (WSOL) aims to train an object locator using only image-level annotations. Recent progress in WSOL has been predominantly driven by visual transformer architectures, which effectively model long-range feature dependencies through self-attention mechanisms and multilayer perceptrons. However, existing transformer-based approaches typically depend solely on location tokens for localization, neglecting valuable semantic insights provided by class tokens, resulting in diffuse activations and imprecise localization. In this paper, we propose a novel framework named Cross-token Guidance TRansformer (CGTR), which enhances semantic coherence and localization precision by facilitating effective interaction between class and location tokens. Specifically, we introduce an Attention Regulation Module (ARM) to globally refine spatial activations using semantic guidance from class tokens, mitigating contextual biases. Additionally, we develop a Filter Regulation Module (FRM) that applies local structural refinement guided by semantic information, enriching the semantic representation of localization maps. Extensive experimental evaluations demonstrate the effectiveness of the proposed CGTR, achieving substantial and consistent performance gains compared with previous approaches on both the CUB-200-2011 and ILSVRC datasets.

ECCV 2026

Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition

Zoey Shu, Jiacheng Yang, Yang Lu, Waishan Qiu, Chuan Li, Da Chen

Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. While recent methods achieve impressive performance, they overlook a serious long-tailed problem hidden in urban-scale datasets, which biases the model towards locations with abundant images and ignores less-visited areas, causing models to systematically favor frequently photographed locations while failing in sparsely covered areas. In this paper, we systematically characterize this imbalance challenge and propose Distribution-Aware Place Recognition (DAPR), a model-agnostic plug-in framework that rebalances gradient contributions across head and tail classes. Additionally, within classification-retrieval pipelines, DAPR applies a multi-scale distance search mechanism to compute per-class distributional compactness, providing complementary gains at the retrieval stage. On the large-scale SF-XL benchmark, our framework outperforms the previous classification-retrieval baseline by 18.3% on test set v1, and 6.7% on test set v2. As a plug-in module, it achieves consistent improvements across representative VPR methods on SF-XL, MSLS, and Pitts30k, demonstrating broad generalizability across different methods and benchmarks.

ECCV 2026

Beyond Final Answers: CRYSTAL Benchmark for Transparent Multimodal Reasoning Evaluation

Wayner Barrios, SouYoung Jin

Modern multimodal large language models (MLLMs) achieve impressive results on vision-language benchmarks, yet existing evaluations judge only final answers, making shortcuts indistinguishable from genuine understanding. We introduce CRYSTAL (Clear Reasoning via Yielded Steps, Traceability and Logic), a diagnostic benchmark with 6,372 instances that evaluates multimodal reasoning through verifiable intermediate steps. We propose two complementary metrics: Match F1, which scores step-level precision and recall via semantic similarity matching, and Ordered Match F1, which further penalizes disordered reasoning chains. References are constructed through a Delphi-inspired pipeline where four independent MLLMs generate trajectories, aggregated via semantic clustering and validated through human quality gates. Evaluation of 20 MLLMs, including commercial frontier systems not used during benchmark construction, reveals systematic failures invisible to accuracy: universal cherry-picking (precision far exceeds recall), non-monotonic scaling trade-offs, and disordered reasoning, where even the strongest reasoners preserve under 60% of matched steps in correct order. Beyond evaluation, we propose the Causal Process Reward (CPR), a multiplicative reward that couples answer correctness with step-level alignment, and CPR-Curriculum, which progressively increases reasoning difficulty during training. CPR-Curriculum achieves +32% Match F1 via GRPO where additive reward strategies fail, improving reasoning without manual step annotation.

ECCV 2026

STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation

Hesham Syed, Yun Liu, Guolei Sun, Jing Yang, Henghui Ding, Xue Geng, Xudong Jiang

Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense spatiotemporal tokens whose quadratic selfattention cost makes long-video processing prohibitive. Existing methods address this through token compression, yet typically operate on encoder features lacking temporal context, constraining selection before content redundancy can be reliably assessed. Informed compression requires contextual awareness, but acquiring that awareness at full resolution incurs the same quadratic cost compression aims to reduce. State-space models resolve this constraint, as their linear recurrence selectively conditions each token on temporal context at O(T) cost, producing representations where content redundancy becomes assessable. Building on this, Selective SpatioTemporal Aggregation and Compression (STAC) enriches features via decoupled bidirectional spatial and causal temporal scanning, leveraging recurrence-derived redundancy for hierarchical compression with adaptive thresholds optimised with segmentation objective. STAC achieves 85% token reduction and 1.8× speedup while surpassing compression-free baselines on reasoning segmentation benchmarks in a zero-shot streaming-compatible setting. Code is available here.

ECCV 2026

Provable and Robust Wavefront Sensing via Self-Reference Interferometry

Nebiyou Yismaw, Vishwanath Saragadam, Aswin C. Sankaranarayanan, M. Salman Asif

Wavefront sensing involves estimating the phase and intensity of light, enabling a wide range of imaging applications, from adaptive optics and astronomy to biomedical imaging. Since conventional image sensors can only measure the spatial intensity distribution, phase retrieval arises as the central problem in wavefront sensing. Conventional interferometric approaches like phase-shifting interferometry (PSI) can recover phase information, but they rely on a stable reference beam that is difficult to realize in practical settings. To overcome this limitation, we propose a novel self-reference framework that relies on interference between shifted copies of the incoming wave; this results in pairwise phase differences between shifted pixels. We formulate an analytical solution for the complete phase retrieval based on the propagation of these differences across a connected graph. Furthermore, we provide a theoretical analysis of optimal measurement patterns, proving that co-prime shifts guarantee a connected graph and bound worst-case error accumulation, yielding a provably robust method. Extensive simulations demonstrate that complete phase profiles can be recovered from as few as eight shifted measurements, outperforming several existing approaches. Finally, we validate our framework using a hardware prototype, demonstrating real experiments for optical phase profile recovery, auto-refocusing, and imaging through scattering media.

ECCV 2026

DSAR: Dual-Stream Autoregressive Modeling of Temporal Cloth Dynamics for Photorealistic Animatable Avatars

Xiong Haozhong, Yao Yu, Yu Zhou, Sidan Du

Creating photorealistic and temporally coherent animatable human avatars from RGB videos remains challenging. Current methods struggle to capture realistic cloth dynamics, producing over-smoothed appearance or severe artifacts on out-of-distribution poses. This limitation stems from a fundamental oversight: existing approaches neglect the temporal causality inherent in cloth physics, where current states emerge from previous states through temporal evolution rather than instantaneous skeletal configurations alone. Without explicit modeling of this causal structure, networks learn pose-appearance correlations instead of motion evolution, leading to poor generalization. We introduce a dual-stream autoregressive framework that explicitly models both observable geometric information and implicit internal state. The geometric stream propagates surface displacement from the previous frame, while the state stream fuses current features with historical states retrieved from a memory bank. Motion-adaptive aggregation handles spatiallyvarying dynamics, and adaptive regularization balances smoothness with flexibility.Experiments on challenging datasets demonstrate significant improvements in rendering quality, temporal consistency, and generalization to motion patterns beyond training distributions, validating that dual-stream temporal modeling enables realistic cloth dynamics.

ECCV 2026

Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts

Nuoyan Zhou, Zhijun Tu, Lei Yu, Kun Cheng, Jie Hu, Nannan Wang, Xinghao Chen

VisualAutoRegressivemodeling(VAR)haspioneeredacoarse-to-fine multi-scaleautoregressivegenerativeparadigm,demonstratingstrongcapabilities in image generation. However, VAR still suffers from inherent deficiencies in multi-scale representation learning. Specifically, lower scales primarily capture global semantics, while higher scales focus on fine-grained details. Employing a shared architecture across scales induces optimization conflicts. Moreover, due to the causal autoregressive process, inaccurate semantics at early scales can propagate and significantly degrade the final output. To address these issues, we introduce a scale-aware token-routed Mixture of Experts (MoE) architecture, allowing scale-adaptive expert selection, thereby facilitating decoupled representation learning across scales. In addition, we enhance semantic modeling at early scalesbyincorporatingexternalself-supervisedfeatures.Unlikenaivealignment, we analyse and design a residual feature aggregation scheme tailored to the VAR paradigm. Extensive experiments show that our method significantly improves both training efficiency and generation quality. On the ImageNet 256×256 benchmark, our model achieves a superior FID compared to the dense baseline while requiring only half of the default training epochs and a smaller parameter budget, with a merely marginal increase in training cost. Moreover, the performance gap further widens with larger training epochs.

ECCV 2026

InstGS: Shared-Template Gaussian Instancing for Object-Redundancy-Free Rendering

Zi&amp, #x27, Ang Lu, Qian Zhang, Kang Du, Dong Liang, John Li, Xinyao Wei, Zeyu Wang, Jinyuan Jia

3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have demonstrated remarkable capabilities in photo-realistic novel view synthesis. However, their practical adoption is often hindered by substantial storage requirements and limited rendering efficiency, particularly for scenes with repetitive structures. While existing acceleration methods primarily focus on optimizing individual Gaussian primitives or neural network architectures, they fail to address the fundamental redundancy inherent in repetitive content. To overcome this limitation, we introduce InstGS, the first Gaussian instancing-based accelerated rendering framework. To eliminate redundancy at the representation level, we perform gradient-driven cross-frame instance segmentation to group similar Gaussians into reusable components. A shared Gaussian template with instance-specific offsets is optimized to replace all similar instances, yielding substantial memory saving with negligible loss in visual fidelity. Extensive experiments demonstrate that InstGS achieves high-quality, high-frame-rate, and low-memory rendering performance.

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