NeurIPS 2026 Papers

Generating high-quality and controllable camera motion is essential for AI-assisted cinematography, video synthesis, and 3D scene understanding. We introduce TKCAM, a Text- and Keyframe-conditioned CAMera-motion synthesis framework based on generative masked modeling. To overcome the instability of direct 3D regression, we formulate camera dynamics as continuous 12-DoF kinematic sequences and discretize them into hierarchical motion tokens via a Residual Vector Quantizer (RVQ). A two-stage masked transformer architecture then learns to reconstruct and refine these tokens, utilizing explicit self- and cross-attention modules for robust multi-modal conditioning. A central feature of our framework is animator-style keyframe control: users can provide free-form text prompts alongside sparse key poses, and TKCAM seamlessly inpaints temporally coherent in-between trajectories. Furthermore, to advance evaluation standards, we curate RealEstate10K-Cap, a large-scale text-camera dataset, and establish a comprehensive cross-domain benchmark with a Universal CLaTr Evaluator. Extensive experiments demonstrate that TKCAM significantly surpasses recent state-of-the-art baselines on Fréchet Inception Distance (FID), text-motion matching scores, and retrieval metrics (R@K), producing cinematographically plausible motions while enabling precise spatial guidance. Our code, models, and dataset will be open-sourced upon acceptance.

Watermarking text-to-image diffusion models provides a practical mechanism for provenance tracking and responsible deployment. Post-processing methods are easy to deploy but vulnerable to image-space transformations. In-processing methods improve robustness by embedding the watermark within the denoising trajectory. Yet most existing methods inject the signal into the initial noise or a fixed intermediate latent, without controlling how it evolves during the remaining denoising steps. This can entangle watermark preservation with text-guided generation, risking weaker verification or degraded content fidelity. We propose TIDE, a trajectory-aware watermarking method for text-to-image diffusion models. TIDE injects a learnable watermark into an intermediate latent and jointly optimizes it with an auxiliary watermark condition, using preservation and detection objectives to limit deviation from the unwatermarked reference while maintaining separable watermark evidence. During sampling, TIDE estimates recent text-guidance directions and redirects watermark guidance toward the residual component less aligned with this local text subspace, reducing avoidable interference with semantic generation. Verification uses null-prompt partial inversion to recover the injection latent and match its masked coefficients to the target watermark. Experiments show that TIDE achieves reliable verification on unattacked images, improves robustness under common attacks, and better preserves semantic and visual fidelity.

Diffusion models have achieved remarkable success in image, video, and 3D content creation. However, extending these successes to native 4D model generation remains challenging due to the intricate coupling between temporal geometry and appearance dynamics. Existing approaches typically generate 4D content by synthesizing videos from novel viewpoints, yet they often suffer from poor spatio-temporal consistency across views and time. To address these limitations, we introduce TESLA, a novel feedforward approach for generating native 4D Gaussian Splatting (4DGS) representations. Our key insight is to decouple temporal geometry and appearance generation through a temporally structured latent representation. This decomposition allows us to first establish coarse temporal geometry that captures fundamental structural dynamics, and then synthesize fine-grained appearance details conditioned on this geometric scaffold. To bridge this decoupled representation to the final output, we design a native 4DGS decoder that directly transforms our temporal features into native 4DGS representations, enabling flexible temporal rendering across arbitrary viewpoints and timesteps. Trained on a large collection of curated animated sequences, we extensively evaluate TESLA on the 4DGS generation task, showing superior performance over existing baselines.

To consider synthesizability in structure-based drug design (SBDD), a recent approach has proposed to co-design synthetic pathways with molecular conformers of ligands. However, the intermediate conformers are predicted based solely on the current intermediate molecule, without information about the synthons that will be added later along the synthetic pathway. As a result, the intermediate conformers do not adequately reflect the upcoming substructures, creating the geometric mismatch that leads to cumulative errors along the synthetic trajectory. To this end, we propose SYnthon Contrastive LEarning (SYCLE), a synthesizable SBDD framework that injects future synthon information into intermediate conformer prediction via contrastive learning. SYCLE further utilizes this information in the pathway generation policy to guide synthon selection. SYCLE achieves state-of-the-art binding affinity across all 15 LIT-PCBA targets. On CrossDocked2020, SYCLE attains -9.80 kcal/mol and an AiZynthFinder success rate of 68.8\%, nearly double that of the best baseline.

Generating inputs for systems that process highly structured data, such as compilers, is difficult due to the dual of syntactic and semantic constraints. Traditional approaches rely on manually crafted rules that require deep domain expertise and are difficult to maintain, while modern machine learning methods have to learn syntax and semantics simultaneously which is challenging. We present Synsema, a novel approach that combines insights from both worlds by reformulating the generation of semantically valid programs as a syntax-guided reinforcement learning task. By guaranteeing syntactic validity through the grammar, the agent needs to only learn the language's semantic rules, sampling from a drastically reduced space. We implement our approach in the context of Java and Rust which both exhibit strict semantic requirements, and train an agent to generate interesting and well-formed programs using fine-grained semantic feedback. Applied to compiler testing, we systematically compare different syntax-guided generation procedures with an unconstrained baseline and show that by restricting the sampling space, Synsema learns semantic rules efficiently. While an average of only 0.03% of programs generated by the baseline are semantically valid, Synsema produces a diverse set of programs with a 7.29% success rate that trigger 2.6x more compiler behaviors. In addition, Synsema discovered 5 previously unknown bugs in production compilers, validating the practical impact of our approach.

Synergistic multi-modal question generation aims to generate questions grounded in synergistic semantics distributed across multiple modalities, rather than being specified by isolated information from any single modality alone. In this setting, modality-specific cues and cross-modal shared semantics must be jointly well-organized so that the generated question reflects synergistic multi-modal contribution and captures genuinely synergistic information. However, this task faces two core challenges: *how to disentangle multi-modal information for synergistic information modeling*, and *how to preserve synergistic contribution during question generation*. To address these challenges, we propose **SynMQG**, a synergistic dependency-aware framework derived from a mutual-information-based theoretical analysis, which integrates disentangled representation learning with synergy-aware optimization for synergistic multi-modal question generation. Specifically, SynMQG disentangles the multi-modal context into visual-specific, textual-specific, and shared semantics, thereby explicitly organizing heterogeneous information for synergistic information modeling. Based on these disentangled representations, it further introduces a reasoning chain as an intermediate scaffold to structure cross-modal information before generation. Moreover, SynMQG proposes GRPO with a mutual-information-based synergy reward, which explicitly measures whether the generated question preserves effective contribution of synergistic information, encouraging the model to generate questions that depend on synergistic multi-modal semantics rather than superficial single-modal cues. Experiments on ScienceQA and MultimodalQA show that SynMQG consistently outperforms representative MQG baselines across standard text-generation metrics, MLLM-based evaluation, and human evaluation, while generating questions with stronger synergistic multi-modal grounding and higher multi-modal dependency. The source code is available at https://anonymous.4open.science/r/MQG-8E57.

Synthetic text generation with Differential Privacy (DP) guarantees emerges as a principled approach that can enable the sharing of sensitive datasets across institutional and regulatory boundaries, while bounding the risks of re-identification and membership inference. LLM-based methods deliver promising results; however, comparisons are exacerbated by differing evaluation setups and "private" datasets, potential pre-training contamination is not considered and guarantees are not verified with DP audits. To advance this field, we introduce a unified evaluation framework with standardised utility and fidelity metrics and privacy audits, encompassing nine curated datasets that capture domain-specific complexities such as technical jargon, long-context dependencies, and specialised document structures. In a large-scale empirical study, we benchmark LLM-based state-of-the-art DP text generators of varying sizes (between 1--8B). Our results indicate that DP synthetic text generation remains an unsolved challenge, with quality deteriorating more as the private datasets deviate further from the generators' pre-training corpora. Our novel synthetic text membership inference attack (MIA) explains this observation: Synthetic data quality is overestimated when LLMs have been pre-trained---without DP---on portions of the "private" data to be generated. Finally, our work provides the first quantitative evidence that this "public pre-training and private generation" paradigm invalidates the guaranteed privacy bounds of real-world private datasets.

Image tokenizers play a central role in modern generative models, where the structure of the latent space critically determines the downstream generation performance. A key but underexplored property of effective latent representations is spectral organization: the ability to encode information across frequency components. In this work, we introduce structured state-space regularization, a principled approach for inducing spectral structure in latent spaces. We derive a regularization objective by revisiting state-space models (SSMs) as dynamical systems over compressed representations. This perspective reveals that hidden states of SSMs follow basis function dynamics under predefined input transformations, resulting in a novel regularizer form that enforces the latent space to capture spectral components of images. Experiments demonstrate that our regularizer improves the generative performance of image tokenizers while incurring only minimal loss in their reconstruction fidelity.

Visual anagram is an intriguing form of art creation wherein a single image presents different conceptual interpretations under transformations such as flipping or rotation. Recent work has achieved visual anagram synthesis by leveraging pretrained text-to-image (T2I) diffusion models, yet still suffers from several key limitations including computational inefficiency, suboptimal aesthetic quality, and weak semantic fidelity and expressiveness. This work focuses on generating visual anagrams with substantially improved visual quality at minimal computational cost, thereby advancing intelligent creation of illusionary digital art. To increase image resolution while reducing time overhead, we adapt the cutting-edge parallel denoising algorithm from pixel-based T2I model to the adversarially distilled latent-based one, and accordingly propose a structure-semantic co-optimization (S2CO) framework to counteract the consequent visual degradation. As the core of our approach, S2CO framework comprises three key innovations: (\romannumeral1) null-text structure alignment optimization; (\romannumeral2) semantic enhancement optimization; (\romannumeral3) attention-guided noise fusion. Building upon these components, our method dubbed \textbf{S2CO-Anagram} is able to generate higher-resolution anagram images with noticeably superior visual harmony and semantic faithfulness than related SOTA approaches, all while achieving substantially faster inference speed. Code will be publicly available.

We propose Structural Self-Teaching (SST), a novel framework that enhances compositional image generation in unified multimodal models (UMMs) by leveraging their internal understanding path as a source of structural supervision. The motivation of this work is the observation that the generation path of UMMs often struggles with dense compositional prompts, leading to missing objects, attribute binding failures, and spatial errors. To bridge this gap, our key idea is to convert the phrase-level grounding inherent in the understanding path into training-time structural signals. Specifically, we extract phrase-conditioned support masks from internal attention maps derived from the understanding path and employ them through two complementary mechanisms: 1) phrase-local contrastive alignment to synchronize generated features with their corresponding phrase-level grounding, and 2) a lightweight structural scaffold for spatial modulation of intermediate generation states. By employing attenuated scaffold dropout during training and removing the scaffold branch at inference, our approach improves compositional fidelity across object, attribute, and spatial constraints. Crucially, the framework requires no external structural annotations for training or additional inputs at test-time, preserving the original generation interface. Extensive experiments across multiple UMMs demonstrate that our method consistently improves compositional fidelity while maintaining the efficiency of a lightweight tuning setup.

Existing image generation models face critical challenges regarding the trade-off between computation and fidelity. Specifically, models relying on a pretrained Variational Autoencoder (VAE) suffer from information loss, limited detail, and the inability to support end-to-end training. In contrast, models operating directly in the pixel space incur prohibitive computational cost. Although cascade models can mitigate computational cost, stage-wise separation prevents effective end-to-end optimization, hampers knowledge sharing, and often results in inaccurate distribution learning within each stage. To address these challenges, we introduce a unified multi-stage generative framework formulated under the \emph{stochastic interpolant} formalism with our \textbf{Conditional Dependent Coupling} strategy as a Flow-Matching variant. It decomposes the generative process into interpolant trajectories at multiple stages, ensuring accurate distribution learning while enabling end-to-end optimization. Importantly, the entire process is modeled as a single unified Diffusion Transformer, eliminating the need for disjoint modules and also enabling knowledge sharing. Under explicitly stated assumptions, we provably reduce both transport cost and asymptotic inference time, we empirically validate the underlying NFE--transport-cost and per-evaluation-cost relations. Extensive experiments demonstrate that our method achieves competitive fidelity and substantially lower wall-clock cost in the low-NFE regime across multiple resolutions.

Text-guided image editing with diffusion models struggles with spatial edits (e.g. manipulating objects, changing viewpoints, manipulating occlusions) that depend on the 3D structure understanding of a scene. Marigold and follow-up work demonstrate that a pretrained diffusion backbone already harbors strong geometric priors that can be activated for depth estimation, yet how to redirect these priors toward editing remains unresolved. Through per-layer gradient probing, we discover that the editing and depth objectives are compatible in early transformer layers but actively conflict in late layers, explaining why naive depth-conditioned editing either ignores geometry or degrades visual quality. We present StitchEdit, which first activates the backbone's geometric capability via a lightweight depth-denoising adapter, then selectively fine-tunes this adapter for editing using a probe-guided per-layer learning rate schedule that preserves depth knowledge where it helps and prunes it where it conflicts. On the proposed StitchEditBench, a benchmark of 150 geometry-dependent edits spanning spatial transformation, occlusion manipulation, and viewpoint change, StitchEdit consistently surpasses both instruction-based and depth-conditioned baselines on perceptual quality and geometric fidelity.

Recovering executable CAD programs from 3D meshes is fundamentally challenging due to the long-horizon, compositional nature of CAD construction and the need for precise estimation of both discrete operations and continuous parameters. Existing learning-based methods primarily treat mesh-to-CAD reconstruction as one-shot sequence prediction and are largely limited to simple sketch-extrude pipelines, restricting operation diversity and preventing the use of intermediate geometric feedback, with no mechanism for refining generated programs. To address this limitation, we introduce StepCAD, a generative optimization approach that performs step-by-step, geometry-conditioned program synthesis followed by explicit refinement in program space. Given an input mesh, StepCAD first predicts construction sequences using a state-conditioned CAD policy conditioned on both target and intermediate geometry, then refines them through IoU-guided tree search over local program edits. To address the limited operation diversity in prior work, we further introduce ARCADE-1.5M, a large-scale dataset of 1.5M executable CAD programs with diverse operations, long-horizon sequences up to 150+ operations, and 12.5M intermediate state-action transitions. Experiments on multiple CAD reconstruction benchmarks show that StepCAD achieves state-of-the-art reconstruction accuracy, validity, and robustness, with up to 87.2\% relative IoU improvement over the best prior method and increasingly larger gains as shape complexity increases.

As state-of-the-art text-to-image flow models have matured to deliver near-photorealistic quality, controlling what they generate -- e.g., inhibiting harmful content while promoting benign alternatives -- has become a central challenge. The current steering paradigm consists of adding a global steering vector to selected model activations. While functional, a fixed vector applied example-agnostic and uniformly along the entire trajectory cannot adapt to the changing state of the generation, and causes uncontrolled global changes beyond the targeted concepts. We introduce Steering Fields, a generalization of steering vectors that adaptively re-estimates the steering direction at each step of the generative process. Steering Fields operate on latent representations, expose a continuous trade-off between steering strength and content preservation, and are compositional, enabling the simultaneous induction and inhibition of concepts, setting a new state of the art on safety steering benchmarks. Although our formulation prescribes no explicit spatial mask or object-level prior, the trajectory-adaptive estimation naturally preserves local structure, in a manner reminiscent of image editing. In fact, when applied in the VAE latent space, Steering Fields can serve as a structure-preserving image-editing technique that achieves state-of-the-art semantic fidelity (CLIP, VQAScore), while remaining model-agnostic and inversion-free

Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. Despite significant advances in the image domain, video text editing remains largely unexplored: it is a localized task demanding stroke-level precision within small text regions, which compounds the challenges of cross-frame accuracy, temporal coherence, and stylistic fidelity. We introduce SteerVTE, a unified framework that \underline{\textbf{steer}}s a frozen video diffusion model to perform precise \underline{\textbf{V}}ideo \underline{\textbf{T}}ext \underline{\textbf{E}}diting through style and glyph control. Built on a frozen diffusion transformer, SteerVTE attaches a lightweight text context adapter with two complementary modules: a style encoder capturing the original text's visual attributes, and dual-granularity glyph encoders encoding the target text at both the line and character levels. To overcome the inherently weak text rendering priors of video foundation models, we further propose a glyph-aware spatial-focal loss and a three-stage progressive training curriculum that scales from image to video data. To support large-scale training, we also develop an automatic synthesis pipeline and construct SteerVTE-1M, a dataset of one million triplets spanning diverse scenes, fonts, and stylistic effects. Extensive experiments demonstrate that SteerVTE substantially outperforms existing video editing baselines across text accuracy, style consistency, and temporal coherence. Our anonymous project website is available at \url{https://steervte-paper.github.io}.

Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain poorly understood. Here, we analyze a solvable EBM in the high-dimensional limit: the spherical Boltzmann machine (SBM). Combining tools from random matrix theory and dynamical mean-field theory, we: solve exact equations describing the training dynamics of the SBM; compute the Bayesian evidence, which acts as a partition function in parameter space and encodes global properties of the trained model; and uncover cascades of phase transitions that occur both during training and as a function of hyperparameters, related to successive alignment and condensation of the top modes of the coupling matrix to the data. We connect these transitions to sampling-time generative phenomena in a teacher-student scenario, including: sampling temperature tuning, double descent as a function of regularization strength, tempered posterior effects, and out-of-equilibrium effects during training that induce biases in the trained model. We provide numerical evidence demonstrating that all these phenomena appear in standard generative architectures, beyond the SBM.

Diffusion models have been shown to implicitly generate visual content autoregressively in the frequency domain, where low-frequency components are generated earlier in the denoising process while high-frequency details emerge only in later timesteps. This structure offers a natural opportunity for efficient generation, as high-resolution computation on noise-dominated frequencies is largely redundant. We propose Spectral Progressive Diffusion, a general framework that progressively grows resolution along the denoising trajectory of pretrained diffusion models. To this end, we develop a spectral noise expansion mechanism and derive an optimal resolution schedule from the model's power spectrum. Our framework supports training-free acceleration and a novel fine-tuning recipe that further improves efficiency and quality. We demonstrate significant speedups on state-of-the-art pretrained image and video generation models while preserving visual quality.

Recent video generative models can produce photorealistic short clips, but struggle with long-horizon generation under dynamic camera control, often suffering from viewpoint drift, geometric inconsistency, and error accumulation. We propose Joint Appearance-Geometry Diffusion Transformer (JAG), a spatially-grounded causal video diffusion model that explicitly integrates 3D perception for long and consistent video generation. It adopts a novel dual-branch causal architecture that jointly models visual appearance and scene geometry. Building upon this formulation, we introduce Self Geometry Forcing, a geometry-aware distillation scheme that conditions current generation on self-predicted geometric structures as persistent long-term memory and imposes self-verified geometric rewards on intermediate rollouts, effectively mitigating error accumulation over long horizons. Extensive experiments show that JAG can generate long videos with superior coherent appearance and geometry at constant per-frame computation, improving long-horizon controllability, geometric consistency, and visual fidelity over previous state-of-the-art methods.

Recent 3D indoor scene generation methods often produce layouts with limited spatial feasibility, where pathways are blocked and objects are difficult to access or use, restricting their applicability in virtual reality and embodied environments. To address this challenge, this paper presents a new paradigm for 3D indoor scene generation that explicitly models interaction feasibility through a carefully designed geometric abstraction, termed the Interaction-Oriented Human Proxy (IOHP). The IOHP serves as a compact yet spatially expressive representation for human interaction occupancy, and can be directly inferred by large language models without task-specific training. Building on IOHP, we formulate layout optimization as a unified objective that resolves geometric conflicts between the proxy and scene elements, while using the proxy as a bridge to preserve functional relationships among furniture, thereby improving overall usability and accessibility. Benefiting from this design, our method eliminates reliance on data-driven human preference learning and complex layout-planning pipelines, producing accessible, usable, and spatially coherent scenes in an efficient and stable manner. Extensive experiments demonstrate that our approach significantly improves layout quality and spatial plausibility, highlighting the role of human-centric interaction modeling in 3D scene generation.