Reshoot-Anything

Reshoot-Anything is a self-supervised video reshooting model built on top of Wan2.2-I2V-A14B. Given a source video and a target camera trajectory (encoded as an anchor video), it generates a high-fidelity reshoot that faithfully follows the new camera path while preserving original content, complex dynamics, and temporal consistency — trained entirely on in-the-wild monocular videos.

Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting
Avinash Paliwal, Adithya Iyer, Shivin Yadav, Muhammad Ali Afridi, Midhun Harikumar
Morphic Inc. · arXiv:2604.21776

Source Video	Reshot Video

Model Files

This repository contains two LoRA checkpoints (rank-512, applied to attention and feed-forward layers of Wan2.2-I2V-A14B):

File	Role	Notes
`jan06_scaling_80k_ckpt1400.safetensors`	High-noise expert	Controls early denoising steps. Primarily responsible for camera motion alignment and global scene structure. Trained on ~80k clips with scaling augmentations + 15% synthetic data mixture.
`dec23_v2v_lownoise_black_lora_512_ckpt1000.safetensors`	Low-noise expert	Controls late denoising steps. Responsible for texture fidelity and fine detail. Uses standard black-background anchors, no source reconstruction loss.

Both files are ~9.82 GB each.

Quickstart

1. Clone the repository

git clone https://github.com/morphicfilms/video-to-video.git
cd video-to-video

Follow the Wan2.2 installation guide to set up the environment, or run:

bash setup_env.sh

2. Download the weights

Download the Wan2.2 I2V base weights:

huggingface-cli download Wan-AI/Wan2.2-I2V-A14B --local-dir ./Wan2.2-I2V-A14B

Download the Reshoot-Anything LoRA weights:

huggingface-cli download morphic/reshoot-anything --local-dir ./reshoot-anything-weights

3. Prepare your anchor video

At inference, generate an anchor video by converting your source video to a 4D point cloud, applying the target camera trajectory, and forward-warping to produce the geometric anchor. See the repo's anchor_generation/ scripts for details.

4. Run reshooting

torchrun --nproc_per_node=8 generate.py \
    --task v2v-A14B \
    --size 1280*720 \
    --frame_num 81 \
    --ckpt_dir ./Wan2.2-I2V-A14B \
    --high_noise_lora_path ./reshoot-anything-weights/jan06_scaling_80k_ckpt1400.safetensors \
    --low_noise_lora_path ./reshoot-anything-weights/dec23_v2v_lownoise_black_lora_512_ckpt1000.safetensors \
    --source_video examples/source.mp4 \
    --anchor_video examples/anchor.mp4 \
    --dit_fsdp \
    --t5_fsdp \
    --ulysses_size 8

Note: Refer to the GitHub README for the authoritative argument names and single-GPU usage.

How It Works

Reshoot-Anything adapts the Wan2.2-14B Mixture-of-Experts (MoE) DiT with two key architectural changes:

Dual-stream token conditioning — Both the anchor video V_a (geometric guide) and source video V_s (texture reference) are VAE-encoded and temporally concatenated as tokens into the model's main self-attention mechanism. This outperforms cross-attention for view synchronization by letting the model directly route textures across spatial and temporal positions.

Offset RoPE — A fixed temporal offset of 50 is added to source video token positional embeddings, strictly decoupling source context from the active denoising trajectory.

The model learns implicit 4D spatiotemporal routing — when a target frame requires content occluded in the corresponding source frame, the model locates and re-projects the missing texture from a different timestep in the source video.

Self-Supervised Training Pipeline

Training requires no paired multi-view data. From a single monocular video:

Two independent smooth random-walk crop trajectories are sampled → source V_s and target V_t
V_s[0] is forward-warped via AllTracker dense flow + crop offset → anchor V_a
The triplet (V_s, V_a, V_t) forms the training signal

A hybrid dataset strategy augments the monocular pipeline with a 15% mixture of paired synthetic data from ReCamMaster, enabling generalization to extreme (120°+) orbital camera trajectories.

Training Details

Parameter	Value
Base model	Wan2.2-I2V-A14B (14B MoE)
LoRA rank	512 (attention + FFN)
Training steps	2,000 per expert
Batch size	24
Learning rate	1e-5
Optimizer	AdamW (β₁=0.9, β₂=0.999)
Loss	MSE + 0.1 × L1 source reconstruction
Latent frames	20
Primary data	~100k clips from 30k monocular videos
Synthetic mixture	15% ReCamMaster paired clips

Key augmentations: 3D-aware noise injection into anchor reference frame (magnitude uniform [0, 0.5]), fluorescent pink masked-region backgrounds, random anchor reference frame selection, source token auxiliary reconstruction loss.

Evaluation

Results on 100 five-second videos from OpenSora-Mixkit (16fps, 480p):

Method	CLIP-F ↑	RotErr ↓	TransErr ↓	Mat. Pix ↑	FVD-V ↓	CLIP-V ↑
ReCamMaster	98.49	11.29	19.59	1314.00	732.52	88.91
EX-4D	98.94	3.94	4.21	2188.98	685.63	89.77
TrajectoryCrafter (49f)	98.80	2.26	3.03	1851.80	582.56	92.40
Ours	99.03	2.76	4.23	2720.83	586.24	93.16
Ours (49f)	99.01	2.61	2.73	2737.65	488.22	94.96

Citation

@article{paliwal2026reshootanything,
  title={Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting},
  author={Paliwal, Avinash and Iyer, Adithya and Yadav, Shivin and Afridi, Muhammad Ali and Harikumar, Midhun},
  journal={arXiv preprint arXiv:2604.21776},
  year={2026}
}