Mind the Time: Temporally-Controlled Multi-Event Video Generation (2412.05263v1)

Abstract: Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragraph of text as input. When tasked with generating multiple events described using a single prompt, such methods often ignore some of the events or fail to arrange them in the correct order. To address this limitation, we present MinT, a multi-event video generator with temporal control. Our key insight is to bind each event to a specific period in the generated video, which allows the model to focus on one event at a time. To enable time-aware interactions between event captions and video tokens, we design a time-based positional encoding method, dubbed ReRoPE. This encoding helps to guide the cross-attention operation. By fine-tuning a pre-trained video diffusion transformer on temporally grounded data, our approach produces coherent videos with smoothly connected events. For the first time in the literature, our model offers control over the timing of events in generated videos. Extensive experiments demonstrate that MinT outperforms existing open-source models by a large margin.

Detailed Analysis of "Mind the Time: Temporally-Controlled Multi-Event Video Generation"

The paper by Wu et al., titled "Mind the Time: Temporally-Controlled Multi-Event Video Generation" introduces a novel approach in the domain of video generation. The proposed method, MinT, addresses the challenge of generating videos composed of sequential events with precise temporal control. This paper is particularly meaningful given the limitations of existing video generation models, which struggle to produce multi-event sequences accurately.

MinT stands out by offering temporal control over event sequences—a first in this research area. The core innovation is the binding of each event in a sequence to a specific period, allowing focused and temporally-accurate event representation in the generated video. To achieve this, Wu et al. devised a time-based positional encoding method, Rescaled Rotary Position Embedding (ReRoPE), to model time-aware interactions between event captions and video tokens. This innovation enables the video generator to focus on one event at a time, thus maintaining coherence and smooth transitions between events.

The researchers fine-tuned a pre-trained video diffusion transformer, ensuring that MinT could leverage existing capabilities while expanding into the temporal domain. The model has been evaluated extensively and demonstrates significant improvements over current capabilities in smoothly connecting sequential events within videos. The tests also reflect that MinT maintains high visual quality and subject consistency.

Key Contributions and Results

1. Novel Temporal Control: MinT is presented as the first video generator to support sequential event generation with temporal control, distinguishing it from previous models which typically lack explicit temporal constraints.
2. ReRoPE Technology: The introduction of ReRoPE is a defining feature as it enables the model to focus on frames within an event's time range, ensuring a smooth event transition.
3. Training Strategy: The proposed training strategy includes conditioning the model on scene cuts, facilitating long video and shot transition control, which previous models often overlook.
4. Dataset and Evaluation: Through a dataset and experiments, MinT achieved state-of-the-art multi-event video generation results across both text-only and image-conditioned settings. It was evaluated using a hold-out set and StoryBench, showing improvements in text-to-video alignment and temporal consistency.
5. Prompt Enhancement: An LLM-based prompt enhancer extends short prompts to detailed captions, generating videos with richer motion. This feature underscores the model's ability to synthesize complex scenarios depicted through intricate text descriptions.

Methodology and Evaluation

Wu et al. leverage a novel task formulation, aiming to generate videos containing all given events following their specified time range. A pre-trained text-to-video Diffusion Transformer (DiT) is utilized, enhanced with a temporally-aware cross-attention layer for timestamp control. This innovative architecture enables detailed prompt-driven generation, improving subject consistency and temporal smoothness, aspects routinely problematic in earlier models.

The model's robustness is further demonstrated through evaluations against popular baselines such as CogVideoX, Mochi, and commercial models. MinT's superior performance can be observed in key metrics such as text-to-video alignment and dynamic degree, reinforcing its potential in practical applications.

Broader Implications and Future Work

Wu et al.'s MinT model pushes the boundaries of video generation by introducing precise temporal controls, potentially transforming content creation across multiple domains, from entertainment to educational purposes. The capability to specify exact timings for events can revolutionize how creators approach video content, allowing for unprecedented control over narrative pacing and coherence.

Future research could focus on integrating MinT's temporal modeling capabilities with spatial controls, enhancing the model's applicability to scenarios requiring fine-grained spatial and temporal manipulations. Additionally, exploration into training-free optimization techniques and advanced personalization strategies could further elevate the model's performance and usability.

In conclusion, "Mind the Time: Temporally-Controlled Multi-Event Video Generation" is a critical advancement in video synthesis technology, offering significant improvements in video event coherence and inter-event transition quality. This work sets a new standard for future research endeavors seeking to enhance video generation models with comprehensive time-based controls.