Autoregressive video generation is a rapidly developing research field. It focuses on a frame-by-frame synthesis of learning patterns using spatial arrangements and temporal dynamics. Unlike traditional video creation methods that may rely on pre-built frameworks or manual transitions, the autoregressive model is designed to generate content dynamically based on tokens. This approach is similar to the prediction of the next word in large language models. It provides the potential to unify video, image and text generation under a shared framework by using structural forces of transformer-based architecture.
A major problem in this field is how to accurately capture and model the spatial and temporal dependencies inherent in videos. Videos contain rich structures in both time and space. Encoding this complexity, so that the model can predict coherent future frames remains a challenge. When these dependencies are not modeled well, it can cause frame continuity to break or unrealistic content to be generated. Traditional training techniques (such as random masking) are also struggling. They usually fail to provide a balanced learning signal across frames. When spatial information leaked by adjacent frames leaks, prediction becomes too easy.
Several methods attempt to address this challenge by tuning the autoregressive generation pipeline. However, they often deviate from standard large language model structures. Some people use externally trained text encoders to make the model more complex and relevant. Others bring significant latency in the production process, and decoding is inefficient. Autoregressive models such as phanaki and Emu3 try to support an end-to-end generation. Nevertheless, they still struggle with performance consistency and high training costs. Techniques such as raster scanning orders or global sequence attention also do not scale well to high-dimensional video data.
The research teams of Alibaba Group Damo Academy, Hupan Lab and Zhejiang University introduced Lumos-1. It is a unified model generated by autoregressive videos, which is loyal to large language model architectures. Unlike previous tools, Lumos-1 eliminates the need for external encoders, while there are very little changes in the original LLM design. The model uses MM rope or multi-mode rotation position embedding to meet the challenges of modeling the three-dimensional structure of the video. The model also uses token dependency methods. This preserves the bidirectionality within the framework and the temporal causal relationship between the frameworks, which is more natural with the behavior of the video data.
In MM ropes, researchers extend existing rope methods to spectrum that balance spatial and temporal dimensions. Traditional 3D ropes are improperly focused on frequency, resulting in loss of detail or ambiguous position coding. The MM cycles reorganizes the allocation so that time, height and width each receive a balanced representation. To solve the loss imbalance in framework training, Lumos-1 introduces AR-DF or autoregressive discrete diffusion forcing. It uses time tube masking during training, so the model does not rely too much on unmasked spatial information. This ensures learning in video sequences. The reasoning strategy reflects training, thereby enabling high-quality framework generation without degradation.
The Lumos-1 uses only 48 GPUs and trains 60 million images and 10 million videos from scratch. This is considered memory efficient considering the training scale. The results obtained by this model are comparable to those of the top-level models on the field. It matches the results of EMU3 in the Geneval benchmark. It is performed equivalently with cosmos-video2world on VBENCH-I2V test. It also rivals the output of OpenSoraplan in the VBENCH-T2V benchmark. These comparisons show that the lightweight training of Lumos-1 does not impair competitiveness. This model supports text-to-video, image-to-video, and text-to-image generation. This shows a strong generalization across modes.
Overall, this study not only identifies and solves the core challenges in spatiotemporal modeling of video generation, but also demonstrates how Lumos-1 sets new standards for the efficiency and effectiveness of a unified automated regression framework. By successfully integrating advanced architecture with innovative training, Lumos-1 paves the way for the next generation of scalable high-quality video generation models and opens up new avenues for future multimodal research.
Check Paper and github. All credits for this study are to the researchers on the project.
Join NVIDIA, OpenAI, DeepMind, Meta, Microsoft, JP Morgan Chase, Amgan, Amgan, Aflac, Aflac, Wells Fargo and 100s NVIDIA, OpenAI, Meta, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, Microsoft, and 100s researchers.
Nikhil is an intern consultant at Marktechpost. He is studying for a comprehensive material degree in integrated materials at the Haragpur Indian Technical College. Nikhil is an AI/ML enthusiast and has been studying applications in fields such as biomaterials and biomedical sciences. He has a strong background in materials science, and he is exploring new advancements and creating opportunities for contribution.
