Papers Text-to-Video Generation
“Text-to-Video Generation” 태그가 달린 논문 308편 · 필터 해제
WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation
Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt …
Text-to-Video GenerationNoisEasier: Test-Time Noise Optimization for Text-to-Video Generation
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 rewa…
Text-to-Video GenerationIdentity-Preserving Text-to-Video Generation via Agentic Enhancement and Semantic Repair
Identity-preserving video generation aims to synthesize videos that follow natural-language instructions while maintaining the visual identity of a given subject. Recent commercial video generation models have achieved s…
Text-to-Video GenerationInstruction FollowingMLLM-Guided Semantic Correction for Text-to-Video Generation
Recent advances in diffusion models and Transformer architectures have led to significant progress in text-to-video generation. However, these models often suffer from semantic errors such as missing objects, incorrect a…
Text-to-Video GenerationBeyond Text Conditioning: A Systematic Study of MLLM-DiT Fusion for Video Generation
Diffusion Transformers (DiTs) have become the dominant paradigm for high-fidelity video generation, yet their ability to perform high-level semantic planning remains limited. While hybrid architectures integrating MLLMs …
Text-to-Video GenerationSpatially-Grounded Text-to-Video Generation via Inference-Time Gradient-Free Optimization
Diffusion Transformer Text-to-Video models have achieved remarkable synthesis quality, yet fine-grained spatial controllability remains a significant challenge. While existing training-free methods produce solid overall …
Text-to-Video GenerationCAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generation
Text-to-video (T2V) diffusion transformers (DiTs) are trained with detailed video captions, whereas inference often relies on user prompts rewritten by a prompt enhancer (PE). Prior work has improved generation by optimi…
Text-to-Video GenerationGroupVideo: Multi-Identity Customized Text-to-Video Generation
Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity separation mechanisms often leads to identity confusion in multi-identit…
Text-to-Video GenerationMoving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation
Text-to-video generation has advanced significantly over the past five years through scaling of model size, data, and compute. Unlike model architecture, training data is often underexplored. Real-world data curation is …
Text-to-Video GenerationVideo Generation Models are General-Purpose Vision Learners
Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision? In t…
Text-to-Video GenerationCamera Pose EstimationHyperVAttention: Efficient Sparse Attention with Spatio-Temporal Clustering for Video Diffusion
Video Diffusion Transformers (VDiTs) have demonstrated significant capabilities in high-fidelity video generation. However, their ability to produce long-duration videos is fundamentally constrained by the quadratic comp…
Text-to-Video GenerationYour Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation
Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.g., via reward models or DPO) to align generated content with human aesthetics and improve realism. These signals, however, incur su…
Text-to-Video GenerationDisco-LoRA: Disentangled Composition of Content, Style, and Motion for Multi-concept Video Customization
Video customization based on Text-to-Video (T2V) models aims to learn specific features from reference data to generate controllable videos. While significant strides have been made in image stylization and video motion …
Text-to-Video GenerationPhysics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation
Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical laws. Compounding this is a lack of reliable granular evaluation metho…
Text-to-Video GenerationDomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation
Open domain subject-driven text-to-video (S2V) generation has drawn significant interest in academia and industry. Open domain S2V mainly involves two scenarios: in-domain, which requires retaining the reference subject …
Text-to-Video GenerationCustomizing Video Portraits via Identity-ActionDecoupling
Identity-Preserving Text-to-Video Generation (IPT2V) seeks to synthesize a temporally coherent video from a reference image and a textual description, while simultaneously preserving the subject's identity and allowing f…
Text-to-Video GenerationLooseControlVideo: Directorial Video Control using Spatial Blocking
Precise 3D spatial orchestration in text-to-video generation remains a significant challenge, particularly for multi-object scenes where semantic layout and temporal dynamics are often entangled. While existing depth-con…
Text-to-Video GenerationSpecLoR: Spectral Lookahead Rectification for Motion-Coherent Text-to-Video Generation
Flow Matching has enabled robust text-to-video generation via latent ODE sampling. However, velocity approximation and numerical discretization errors inevitably accumulate, causing sampling trajectories to drift. Conseq…
Text-to-Video GenerationOmniGen-AR: AutoRegressive Any-to-Image Generation
Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, existing methods are typically limited to …
Text-to-Video GenerationText-to-Image GenerationImage EditingDo Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation
Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring retraining or tailored interventions for each new architecture. In this…
Text-to-Video Generation