  Video generation has drawn significant interest recently, pushing the
development of large-scale models capable of producing realistic videos with
coherent motion. Due to memory constraints, these models typically generate
short video segments that are then combined into long videos. The merging
process poses a significant challenge, as it requires ensuring smooth
transitions and overall consistency. In this paper, we introduce Anchored
Diffusion, a novel method for synthesizing relatively long and seamless videos.
We extend Diffusion Transformers (DiTs) to incorporate temporal information,
creating our sequence-DiT (sDiT) model for generating short video segments.
Unlike previous works, we train our model on video sequences with random
non-uniform temporal spacing and incorporate temporal information via external
guidance, increasing flexibility and allowing it to capture both short and
long-term relationships. Furthermore, during inference, we leverage the
transformer architecture to modify the diffusion process, generating a batch of
non-uniform sequences anchored to a common frame, ensuring consistency
regardless of temporal distance. To demonstrate our method, we focus on face
reenactment, the task of creating a video from a source image that replicates
the facial expressions and movements from a driving video. Through
comprehensive experiments, we show our approach outperforms current techniques
in producing longer consistent high-quality videos while offering editing
capabilities.
