Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
ArXivabs/2310.01107(2023),https://api
5 Pith papers cite this work. Polarity classification is still indexing.
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TeleMorpher introduces a training-free pose-warping pipeline plus two LPIPS-based metrics for simultaneous motion and location editing in videos, claiming superior results on in-the-wild and TaiChi data.
RTR-DiT distills a bidirectional DiT teacher into an autoregressive few-step model using Self Forcing and Distribution Matching Distillation, plus a reference-preserving KV cache, to enable stable real-time text- and reference-guided video stylization.
NeuS-E is a post-generation refinement method that uses neuro-symbolic analysis of a formal video representation to detect and correct semantic and temporal inconsistencies in text-to-video outputs, improving prompt alignment by nearly 40%.
IDAG-Edit proposes a training-free method with Layout-guided Attention Modulation and Instance-level Masks for improved temporal consistency and multi-object controllability in diffusion-based video editing.
citing papers explorer
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Functionalization via Structure Completion and Motion Rectification
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
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TeleMorpher: Toward Robust Simultaneous Motion-Location Editing
TeleMorpher introduces a training-free pose-warping pipeline plus two LPIPS-based metrics for simultaneous motion and location editing in videos, claiming superior results on in-the-wild and TaiChi data.
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DiT as Real-Time Rerenderer: Streaming Video Stylization with Autoregressive Diffusion Transformer
RTR-DiT distills a bidirectional DiT teacher into an autoregressive few-step model using Self Forcing and Distribution Matching Distillation, plus a reference-preserving KV cache, to enable stable real-time text- and reference-guided video stylization.
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We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback
NeuS-E is a post-generation refinement method that uses neuro-symbolic analysis of a formal video representation to detect and correct semantic and temporal inconsistencies in text-to-video outputs, improving prompt alignment by nearly 40%.
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IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance
IDAG-Edit proposes a training-free method with Layout-guided Attention Modulation and Instance-level Masks for improved temporal consistency and multi-object controllability in diffusion-based video editing.