NoisEasier: Test-Time Noise Optimization for Text-to-Video Generation

• URL: http://arxiv.org/abs/2608.30194v1
• Date: Mon, 31 Aug 2026 03:17:52 GMT
• Title: NoisEasier: Test-Time Noise Optimization for Text-to-Video Generation
• Abstract summary: Diffusion models have recently advanced text-to-video (T2V) generation, yet they still struggle with fine-grained compositional alignment.<n>We propose NoisEasier, a test-time scaling framework that improves T2V generation through differentiable reward-guided noise optimization.
• Score: 10.472880594575196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have recently advanced text-to-video (T2V) generation, yet they still struggle with fine-grained compositional alignment, such as attribute binding, spatial relations, and object interactions. While reward-based fine-tuning improves alignment, it is susceptible to reward hacking and adapts poorly to new prompt distributions. In this work, we propose NoisEasier, a test-time scaling framework that improves T2V generation through differentiable reward-guided noise optimization without modifying the underlying model. By combining efficient short-step generators with a multi-objective reward formulation, NoisEasier enables stable and practical test-time optimization under realistic inference budgets. Our key insight is that jointly optimizing the entire stochastic trajectory accelerates reward convergence and improves compositional alignment over optimizing only the initial latent, with negligible additional computational and time cost. Experiments on VBench and T2V-CompBench demonstrate consistent improvements across multiple backbones, achieving over 10% average gains on challenging dimensions such as attribute binding, object interaction, and numeracy. Overall, NoisEasier serves as both a flexible alternative and a complementary enhancement to reward-based fine-tuning, establishing test-time scaling as an effective paradigm for controllable text-to-video generation.

Related papers

• Inference-Time Scaling for Joint Audio-Video Generation [38.09471807128537]
  Joint audio-video generation models often require substantial training resources to improve fidelity.<n>Inference-Time Scaling is a promising training-free alternative in single-modality domains.<n>We present the first comprehensive study of ITS for joint audio-video generation.
  arXiv  Detail & Related papers  (2026-06-02T05:41:41Z)
• Anon: Extrapolating Adaptivity Beyond SGD and Adam [25.876261427108673]
  Anon provides the first unified and reliable framework capable of bridging the gap between classical and moderns.<n>Anon is more flexible than hard-tracking and enhances gradient robustness to noise.
  arXiv  Detail & Related papers  (2026-05-04T08:14:51Z)
• AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation [59.964989458924585]
  AdaExplore is an agent framework that enables self-improvement via accumulated execution feedback for performance-critical kernel code generation.<n>In the adaptation stage, the agent synthesizes tasks and converts recurring failures into a reusable memory of validity rules.<n>In the search stage, the agent organizes candidate kernels as a tree and alternates between small local refinements and larger structural regeneration.
  arXiv  Detail & Related papers  (2026-04-17T18:25:03Z)
• Diff3R: Feed-forward 3D Gaussian Splatting with Uncertainty-aware Differentiable Optimization [76.38917994186733]
  Diff3R is a novel framework that bridges feed-forward prediction and test-time optimization.<n>We show that it can be seamlessly integrated into existing feed-forward 3DGS architectures for both pose-given and pose-free methods.
  arXiv  Detail & Related papers  (2026-04-01T15:40:20Z)
• NOVAK: Unified adaptive optimizer for deep neural networks [0.0]
  NOVAK is a gradient-based optimization algorithm that integrates adaptive moment estimation, rectified learning-rate scheduling, decoupled weight regularization, multiple variants of Nesterov momentum, and lookahead synchronization into a unified, performance-oriented framework.
  arXiv  Detail & Related papers  (2026-01-11T13:03:57Z)
• ROOT: Robust Orthogonalized Optimizer for Neural Network Training [47.05662448082334]
  Large language models (LLMs) remain a critical challenge, particularly as model scaling exacerbates sensitivity to imprecision and training instability.<n>We develop a dimension-robustization scheme that enhances robustness through iterations tailored to specific matrix sizes.<n>Second, we introduce an optimization-robustization framework that suppresses outliers noise while preserving meaningful directions.
  arXiv  Detail & Related papers  (2025-11-25T18:48:05Z)
• HVAdam: A Full-Dimension Adaptive Optimizer [18.315990067259253]
  Anon provides the first unified and reliable framework capable of bridging the gap between classical and moderns.<n>Anon interpolates between SGD-like and Adam-like behaviors and even beyond both.
  arXiv  Detail & Related papers  (2025-11-25T13:05:40Z)
• RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling [59.088798018184235]
  textbfRAPO++ is a cross-stage prompt optimization framework.<n>It unifies training-data-aligned refinement, test-time iterative scaling, and large language model fine-tuning.<n> RAPO++ achieves significant gains in semantic alignment, compositional reasoning, temporal stability, and physical plausibility.
  arXiv  Detail & Related papers  (2025-10-23T04:45:09Z)
• Interactive Test-Time Adaptation with Reliable Spatial-Temporal Voxels for Multi-Modal Segmentation [56.70910056845503]
  Multi-modal test-time adaptation (MM-TTA) adapts models to an unlabeled target domain by leveraging the complementary multi-modal inputs in an online manner.<n>Previous MM-TTA methods for 3D segmentation suffer from two major limitations: 1) unstable frame-wise predictions caused by temporal inconsistency, and 2) consistently incorrect predictions that violate the assumption of reliable modality guidance.<n>This work introduces a comprehensive two-fold framework: Latte++ that better suppresses the unstable frame-wise predictions with more informative geometric correspondences, and Interactive Test-Time Adaptation (ITTA), a flexible add-on to empower effortless human feedback
  arXiv  Detail & Related papers  (2024-03-11T06:56:08Z)
• Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers [109.52244418498974]
  We propose a novel textscAdmeta (textbfADouble exponential textbfMov averagtextbfE textbfAdaptive and non-adaptive momentum) framework. We provide two implementations, textscAdmetaR and textscAdmetaS, the former based on RAdam and the latter based on SGDM.
  arXiv  Detail & Related papers  (2023-07-02T18:16:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.