🌐 The First NTP MLLM with Continuous Unified Vision Representations:
Ming-VideoMAR is a concise and efficient decoder-only autoregressive image-to-video model with continuous tokens, composing temporal frame-by-frame and spatial masked generation. Ming-VideoMAR identifies temporal causality and spatial bi-directionality as the first principle of video AR models, and proposes the next-frame diffusion loss for the integration of mask and video generation.
🖼️ First Zero-shot Resolution Scaling for Video Generation:
Ming-VideoMAR replicates the unique capacity of sequence extrapolation from language models to video generation. It supports generating videos of flexible spatial and temporal resolutions that is far beyond the training resolution. This is achieved by solving the training-inference gap and adopting the 3D rotary embeddings.
⚡ Extreme Hihg Training Efficiency:
Ming-VideoMAR proposes the temporal short-to-long curriculum learning and spatial progressive resolution training. It surpasses the previous state-of-the-art (Cosmos I2V) while requiring significantly fewer parameters (9.3%), training data (0.5%), and GPU resources (0.2%), both quantatively and qualitatively.
⚡ Extreme Hihg Inference Efficiency:
Ming-VideoMAR inherently bears high efficiency due to simultaneous temporal-wise KV cache and spatial-wise parallel generation, significantly surpassing the NTP counterpart.
🔗 Accumulation Error Solution:
Ming-VideoMAR employs the progressive temperature strategy at inference time to mitigate the accumulation error.
📌 Updates
[2025.10.17] 🔥 Code and Checkpoint!
We’re thrilled to announce the code and checkpoint release of Ming-VideoMAR !
[2025 09.19] 🎉 Our paper is accepted by NeurIPS 2025.
Ming-VideoMAR achieves sota autoregressive image-to-video generation performance with extremely small training and inference costs.
Quantitative Comparison
Ming-VideoMAR achieves sota performance across the token-wise autoregressive video generation models with sifnificantly lower training cost.
Qualitative Comparison
Ming-VideoMAR achieves better quality and finer details than the Cosmos baseline, even under lower resolution (Ming-VideoMAR:480x768 VS Cosmos:640x1024).
Qualitative Comparison
Ming-VideoMAR first unlocks the resolution scaling ability to flexibly generate higher or lower resolutions beyond the training scope.
Note!
This model is trained with our inner data, and therefore the original dataloader code is tailored for inner oss file system.
If your want to train this model with your own data, you should replace the following Your_DataReader (Line 219 in main_videomar.py) with your own dataloader code.
######################### Load Dataset #########################
dataset_train = Your_DataReader(data_path=args.data_path, img_size=[args.img_size_h, args.img_size_w], num_frames=args.num_frames, file_type=args.file_type) # Replace this with your data reader file
sampler_train = DistributedSampler(dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True)
data_loader_train = DataLoader(
dataset_train,
sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
🖼️ Inference
Run the following command, which contains the script for the inference of VideoMAR.
bash samle.sh
Specifically, take the default inference script of stage2 for example:
