# Vorch-IR: Long-Form Unified Multimodal Identity Replacement Video Generation

arXiv: https://arxiv.org/abs/2608.05648 · HTML: https://mlsanity.com/paper/2608.05648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 29 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion](https://mlsanity.com/paper/2407.01392/) (2024)
- [Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion](https://mlsanity.com/paper/2506.08009/) (2025)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [Seedance 1.0: Exploring the Boundaries of Video Generation Models](https://mlsanity.com/paper/2506.09113/) (2025)
- [LTX-2: Efficient Joint Audio-Visual Foundation Model](https://mlsanity.com/paper/2601.03233/) (2026)
- [SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction](https://mlsanity.com/paper/2310.20700/) (2023)
- [AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks](https://mlsanity.com/paper/2403.14468/) (2024)
- [HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation](https://mlsanity.com/paper/2505.04512/) (2025)
- [Wan-Animate: Unified Character Animation and Replacement with Holistic Replication](https://mlsanity.com/paper/2509.14055/) (2025)
- [Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance](https://mlsanity.com/paper/2502.06145/) (2025)
- [UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer](https://mlsanity.com/paper/2504.11289/) (2025)
- [Taming Teacher Forcing for Masked Autoregressive Video Generation](https://mlsanity.com/paper/2501.12389/) (2025)
- [SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers](https://mlsanity.com/paper/2506.00830/) (2025)
- [SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations](https://mlsanity.com/paper/2512.05905/) (2025)
- [SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation](https://mlsanity.com/paper/2511.19320/) (2025)
- [One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer](https://mlsanity.com/paper/2511.22940/) (2025)
- [SCAIL-2: Unifying Controlled Character Animation with End-to-End In-Context Conditioning](https://mlsanity.com/paper/2606.10804/) (2026)
- [MoCha:End-to-End Video Character Replacement without Structural Guidance](https://mlsanity.com/paper/2601.08587/) (2026)
- [MultiAnimate: Pose-Guided Image Animation Made Extensible](https://mlsanity.com/paper/2602.21581/) (2026)
- [DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning](https://mlsanity.com/paper/2601.21716/) (2026)
- [Consistent and Controllable Image Animation with Motion Linear Diffusion Transformers](https://mlsanity.com/paper/2508.07246/) (2025)

## Abstract (arXiv)

Video identity replacement seeks to transfer the identities of one or more subjects while preserving the motion, expressions, and temporal structure of a driving video. Existing methods largely target single-person settings and often require task-specific structural controls, such as masks or pose representations, limiting their flexibility in general multimodal editing systems. Progress on multi-person replacement is further constrained by the scarcity of paired training data. We present Vorch-IR, a unified framework that supports single- and dual-person identity replacement, with optional background replacement, in a single model. Built on LTX2, Vorch-IR jointly conditions on a driving video, indexed reference images, and a textual editing instruction. The reference images need not match the pose, layout, or spatial configuration of the driving video: their roles as subject or background references are specified through the instruction. Dense visual conditions are fused through self-attention, while a vision-language context establishes semantic correspondence through cross-attention. We further develop an automatic data construction pipeline that synthesizes paired supervision for all four editing settings. Experiments using automatic metrics and pairwise human evaluation demonstrate strong identity preservation, motion fidelity, and temporal coherence across diverse scenarios. A temporal overlapping inference strategy additionally extends the short-clip model to minute-long generation without autoregressive continuation.