LiveStre4m: Feed-Forward Live Streaming of Novel Views from Unposed Multi-View Video

3DMV@CVPRW 2026

Paper | Project Page

Figure 1. Illustration of the proposed LiveStre4m method, a feed-forward model for live-streaming novel viewpoint video from two or more low-resolution input streams.

Introduction

LiveStre4m is a feed-forward model for real-time novel view synthesis (NVS) that enables live streaming from as few as two unposed, multi-view inputs by predicting camera parameters directly from RGB video streams. By combining a multi-view vision transformer for 3D scene reconstruction with a diffusion-transformer interpolation module, the system achieves an average reconstruction time of 0.07s per frame at 1024x768 resolution, outperforming optimization-based dynamic scene representation methods in runtime.

Figure 2. Qualitative results produced by LiveStre4m, synthesizing the target viewpoint using only two neighboring input views, without requiring optimization or ground-truth camera parameters.

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🛠️ Environment Setup

This project requires CUDA 12.1. To install all dependencies, clone the repository and build the Conda environment:

cd LiveStre4m
conda env create -f environment.yml
conda activate LiveStre4m

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Data Preparation

LiveStre4m infers camera geometry on the fly, meaning no COLMAP data or pre-computed camera poses are required only time-synchronized video frames.

To evaluate on benchmarks like Neural3DVideo (N3DV) or MeetRoom, please refer to their original repositories to obtain the raw multi-view video datasets. Once you have the raw videos inside a scene folder, run our fast extraction script:

python prepare_dataset.py --dataset <dataset_name>/<scene_name> --max_frames 300

This will extract and automatically organize the frames into the required directory structure:

<dataset_name>/<scene_name>/
    └── FRAMES/
        ├── t0/                 # Timestep 0 (First frame of the video)
        │   ├── cam_00.png      # Camera views (zero-padded, alphabetically sorted)
        │   ├── cam_01.png      
        │   └── ...
        ├── t1/                 # Timestep 1
        │   ├── cam_00.png      
        │   └── ...
        └── ...

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📦 Model Weights

Download the pre-trained LiveStre4m checkpoint (5GB) and place it in the checkpoints/ directory before running inference.

📥 Download LiveStre4m.pth

Alternatively, you can download it directly via the command line:

# Install the Hugging Face CLI
pip install -U "huggingface_hub[cli]"

# Download the model directly into the checkpoints/ folder
huggingface-cli download Pedro-Quesado/LiveStre4m checkpoints/LiveStre4m.pth --local-dir .

🚀 Inference

Run the novel view synthesis pipeline using the following command:

python INFERENCE.py \
    --dataset <dataset_name>/<scene_name> \
    --ckpt_path checkpoints/LiveStre4m.pth \
    --resolution 640 384

Optional Flags:

• --no_cam_optim: Skip test-time camera optimization and go directly to rendering (useful for quick previews).
• --optim_iters 250: Change the number of camera refinement iterations (default is 1000).

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Results

Dynamic Scene Reconstruction

Quantitative comparison against optimization methods on the Neural3DVideo dataset on a single A100 GPU.

Category	Method	Runtime (s) ↓	PSNR ↑	Camera Free	#Views
Video Optimization	K-planes	48.00	32.17	❌	>19
	4DGS	7.80	32.70	❌	≥19
	Spacetime-GS	48.00	33.71	❌	≥19
Frame Optimization	StreamRF	15.00	32.09	❌	>19
	3DGStream	16.93	32.75	❌	≥19
	IGS	2.67	33.89	❌	≥19
Feed-forward	LiveStre4m (Ours)	0.14	20.64	✅	2

(Note: MeetRoom results follow a similar trend, where LiveStre4m operates in 0.10s using only 2 views.)

Feed-Forward Scene Reconstruction

Comparison of feed-forward, camera-free scene reconstruction methods on a single H100 GPU.

Dataset	Method	Runtime (s) ↓	PSNR ↑	Resolution	Pose Free	#Views
Neural3DVideo	FLARE	0.249	21.45	512x384	✅	2
	LiveStre4m (Ours)	0.062	22.44	512x384	✅	2
	LiveStre4m (Ours)	0.074	21.11	1024x768	✅	2
MeetRoom	FLARE	0.243	16.65	512x384	✅	2
	LiveStre4m (Ours)	0.062	19.32	512x384	✅	2
	LiveStre4m (Ours)	0.074	18.65	1024x768	✅	2

Acknowledgement

We adapted some code from some awesome repositories including FLARE, EDEN, Dust3R, and MASt3R. Thanks for making the code bases publicly available.

Citation

If you find this repository useful, please consider citing:

@misc{quesado2026livestre4mfeedforwardlivestreaming,
      title={LiveStre4m: Feed-Forward Live Streaming of Novel Views from Unposed Multi-View Video}, 
      author={Pedro Quesado and Erkut Akdag and Yasaman Kashefbahrami and Willem Menu and Egor Bondarev},
      year={2026},
      eprint={2604.06740},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={[https://arxiv.org/abs/2604.06740](https://arxiv.org/abs/2604.06740)}, 
}