SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Therefore, we propose a dual interpolation path for generating intermediate latent variables, where the contextual path interpolates the latent variables between the contextual frames to ensure temporal coherence, while the denoising path interpolates latent variables in DDIM [43] denoising process conditioned on interpolated text embedding to improve the semantic coherence. Specifically, to interpolate a new frame  $\mathbf{x}^f$  between  $\mathbf{x}^{f - 1}$  and  $\mathbf{x}^{f + 1}$ , we first sample the intermediate initial latent variable from the same distribution proposed in Section 4.2. For the conditional textual prompt  $\mathcal{T}^f$ , we directly interpolate text embeddings of the previous and next frames.
1) Contextual path. To obtain the context-sensitive latent variable  $\tilde{\mathbf{x}}_t^f$ , we perform linear interpolation between the contextual frames  $\mathbf{x}_t^{f - 1}$  and  $\mathbf{x}_t^{f + 1}$  as follows,
$$
\tilde {\mathbf {x}} _ {t} ^ {f} = k \mathbf {x} _ {t} ^ {f - 1} + (1 - k) \mathbf {x} _ {t} ^ {f + 1}. \tag {7}
$$
2) Denoising path. Then, we perform a linear interpolation between the context-sensitive latent variable  $\tilde{\mathbf{x}}_t^f$  and the latent variable obtained from DDIM conditioned on the interpolated prompt  $\mathcal{T}^f$ . We employ  $m(t)$  as the interpolation coefficient and use the notation of  $\mathbf{P}_{t + 1}^{f}$  and  $\mathbf{D}_{t + 1}^{f}$  mentioned in Section 3. The interpolation coefficient  $m(t)$  varies according to the time step, with smaller values in the earlier denoising steps and increase in the latter steps. The interpolation is formulated as follows,
$$
\mathbf {x} _ {t} ^ {f} = (1 - m (t)) \tilde {\mathbf {x}} _ {t} ^ {f} + m (t) (\sqrt {\alpha_ {t}} \mathbf {P} _ {t + 1} ^ {f} + \mathbf {D} _ {t + 1} ^ {f} + \sigma_ {t} \mathbf {z} _ {t}). \tag {8}
$$
To provide an intuitive conditional probability distribution of  $x_{t}^{f}$ , we present the distributions for the cases when  $m(t) = 1$  and  $m(t) = 0$  respectively as:
$$
p \left(\mathbf {x} _ {t} ^ {f} \mid \mathbf {x} _ {t + 1} ^ {f}\right) = \left\{ \begin{array}{l l} \mathcal {N} \left(k \boldsymbol {\mu} _ {t + 1} ^ {f - 1} + (1 - k) \boldsymbol {\mu} _ {t + 1} ^ {f + 1}, [ k & 1 - k ] \boldsymbol {\Sigma} _ {t + 1} ^ {f - 1, f + 1} \left[ \begin{array}{c} k \\ 1 - k \end{array} \right]\right), & m = 0 \\ \mathcal {N} \left(\boldsymbol {\mu} _ {t + 1} ^ {f}, \boldsymbol {\Sigma} _ {t + 1} ^ {f}\right), & m = 1 \end{array} \right. \tag {9}
$$
where  $\pmb{\Sigma}_{t + 1}^{f - 1,f + 1}$  is the covariance matrix of  $\mathbf{x}_{t + 1}^{f - 1}$  and  $\mathbf{x}_{t + 1}^{f + 1}$ .
4.4 Extensions
Our method exhibits high extensibility and seamless integration with existing LDM-based methods. We demonstrate the versatility of our approach through some applications: (1) Personalization. By combining with LDM-based personalization methods [9, 34], our approach can generate videos with user-provided concepts. Here we choose DreamBooth [34] as an example, as shown in the top Figure 1, our method generates a video of "A prince is riding a horse [V]" with the concept [V] as "modern Disney style". (2) Making an image move. With the help of DDIM inversion [43], we can inverse the latent variable from an image, then generate a sequence of frames that continues the image. For example, starting from a still image of the scenery of a river, our method further generates a video depicting the process of freezing, as illustrated in Figure 1.
Implementation Settings. We develop our Free-Bloom based on LLM as ChatGPT [26] and LDM as Stable Diffusion [33] with its pre-trained v1.5 weights. We first generate a video of  $f = 6$  length with  $512 \times 512$  resolution, then we iteratively interpolate between the most distinguished frames with  $k = 0.5$  for the first interpolated frame. For  $m(t)$ , we set  $m(t) = 0.1$  when the denoising time  $t \geq \tau^{*}$  and  $m(t) = 1$  when  $t < \tau^{*}$ . Notice that our method actually can be adapted to generate longer videos. To enhance image quality in practice, we add fixed negative prompts and upscale our resulting frames with an image super-resolution network ESRGAN [50]. All experiments are performed on a single NVIDIA GeForce RTX 3090Ti.
5.1 Baseline Comparisons
Qualitative Results. We showcase examples of Free-Bloom in Figure 3 and compare them with that generated by the zero-shot T2V-Zero [17] and VideoFusion [21], which demonstrate the most outstanding overall performance in the user study. More comparisons are included in the Supplementary. Video generation shown in region A demonstrates the following observations: (1) Our method vividly depicts the complete imagery of a volcanic eruption or the sequential motion of a teddy bear jumping into the water, exhibiting the capacity to generate semantic meaningful frame sequences, in which the visual elements, actions, and events are aligned with the input prompt as well as the contextual narrative. (2) In addition, our method shows temporal coherence and identical coherence while maintaining high fidelity for single frames. (3) Although the T2V-Zero method maintains overall content consistency between frames, it fails to depict sequential events. Furthermore, the subject would easily be distorted as the length of the video increases. (4) VideoFusion, on the other hand, demonstrates impressive temporal coherency between frames as it is trained on large-scale datasets, and it also presents a certain level of grasp of events. However, this training on the vast video dataset also significantly degrades the fidelity and quality of individual frames.
For interpolation results shown in region B, we present one latest state-of-art Video Frame Interpolation method AMT [18] pre-trained on Vimeo90K [56] for comparison. AMT fails to comprehend the subject information between the two target frames. As a result, it blurs the different parts of the teddy bear's body, failing to capture the intermediate motion. In contrast, our method fills in the content gap with a serial of continuous motion of the bear from the air to the water, maintaining fidelity in the intermediate frames while ensuring content consistency.
Quantitative Results are reported with automatic metrics and the user study in Table 1. We adopt three publicly available diffusion-based methods, Text2Video-Zero [17], VideoFusion [21], and LVDM [10] as baselines. VideoFusion and LVDM are both trained methods while the former is
A) Video Generation "Teddy bear jumps into water."
Figure 3: Qualitative comparison. A) Our method can generate semantic meaningful frame sequences when conditioned on the same prompts. B) We interpolate two frames into the frame sequence by taking the two images within the same color box in part A as the start and the end.
trained on both large-scale image datasets ImageNet [6], LAION5B [38] and a large-scale video dataset WebVid-10M [2], and the latter has been trained on a 2-million subset of the WebVid-10M.
For automatic metrics, we use CLIP [29] to evaluate the similarity correlation between the input prompts and the visual content of generated frames. Recall that our method put an emphasis on the overall narrative semantic coherence, therefore we also compute the CLIP score between each frame and its corresponding prompt generated by LLM(\*). We can observe that although comparing each frame against the input prompt overlooks the potential of our approach, our method demonstrates good performance when frames are compared with the frame-level video prompts.
For the user study, the participants are instructed to rate the fidelity, temporal coherence, and semantic coherence on a scale of 1 to 5 and give a comprehensive ranking. According to the user study, although our method may not perform as well as trained methods in terms of temporal continuity, it has received high recognition in all other dimensions of video quality.
Table 1: Quantitative Results. * for CLIP score on serial prompts.
Method	Training-Free	Automatic Metric	User Study
CLIP Metrics↑	Fidelity ↑	Temporal ↑	Semantic ↑	Rank ↓
VideoFusion [21]	0.483	3.436	3.889	3.267	2.317
LVDM [10]	0.480	3.289	3.650	3.242	2.567
T2V-Zero [17]	✓	0.479	3.486	2.783	3.025	3.033
Ours	✓	0.477 / 0.482*	4.133	3.267	3.867	2.083
5.2 Analysis of Our Pipeline
In Figure 4 and Figure 5, we conduct a comprehensive analysis of the modules in our proposed pipeline. The top row showcases our final results.
Figure 4: Analyze for the effects of Free-Bloom (A) Without joint noise sampling, (B) Without shifting self-attention, (C) Serial Prompting for Text2Video-Zero, (D) Without attention to the current frame itself, and (E) Without the step-aware shift strategy.
A) Without joint noise sampling. In A, we replace the joint noise initialization with the independent initialization, resulting in frame sequences with inconsistent image content. B) Without shifting self-attention. In B, we denoise the frames from the proposed joint noise initialization but use the raw LDM without modifying self-attention layers. The frames are similar naturally in some cases, as the joint noise contains a portion of unified noise. However, without attention to contextual frames, it is difficult to maintain identical coherence of both the foreground and background content, let alone temporal continuity. C) Serial Prompting for Text2Video-Zero. In C, we adapt Text2Video-Zero [17] to enable the input of frame-level video prompts so that each frame is generated and conditioned on a distinct prompt. The results show that it is challenging for the current method to comprehend serial prompts effectively, resulting in "moving images". D) Without attention to the current frame itself. In D, we replace all self-attention layers with spatio-temporal attention layers proposed in TAV [53]. The resulting frames exhibit improved temporal coherence, demonstrating smoother transitions between frames. However, the frames appear almost identical, creating a sequence of meaningless and jittery frames based solely on the first frame, which does not align with the intended temporal semantics. Additionally, prolonged contextual attention in long sequences can significantly compromise the fidelity of individual frames. As shown in the case of Iron Man, the last frame presents an incomplete leg. E) Without the step-aware shift strategy. Based on D, we further concatenate the attention to the current frame with contextual attention but without shifting it along time steps. Although the results remain inconsistent with semantics due to the absence of the inference strategy that varies across the denoising time step, the fidelity of images is improved.
Dual-path interpolation. We show the influence of balancing timing coefficients between the dual interpolation path in Figure 5. When  $\tau^{*}$  approaches 1,  $m(t)$  remains relatively small throughout the entire denoising process, indicating that the contextual path primarily determines the interpolated results. The resulting image presents
Figure 5: Analyze for interpolation.
blurring and ghosting artifacts, failing to generate a frame with correct semantics. Conversely, heavily relying on the DDIM denoising path with larger values of  $m(t)$  causes substantial deviations in content compared to the contextual frames. Our method adopts smaller values of  $m(t)$  in the early steps to prioritize coarse-grained shapes and layout and increases  $m(t)$  in the latter steps to improve temporal consistency. As a result, we achieve the benefits of both paths by appropriately setting  $m(t)$ .
In this paper, we devise a novel zero-shot and training-free text-to-video approach, which mainly focuses on improving the narrative of the progression of events. Our proposed pipeline effectively harnesses the knowledge from the pre-trained LLM and LDM and produces highly semantic coherent videos while also maintaining temporal coherence and identical coherence. Limitation: we look forward to further research on text-to-video generation, however, it should be acknowledged that there can be ethical impacts like other generative models. As we adopt ChatGPT and Stable Diffusion v1.5, our method may inherit the bias of those two models.
Acknowledgment: This work was supported by the National Natural Science Foundation of China (No.62206174), Shanghai Pujiang Program (No.21PJ1410900), Shanghai Frontiers Science Center of Human-centered Artificial Intelligence (ShangHAI), MoE Key Laboratory of Intelligent Perception and Human-Machine Collaboration (ShanghaiTech University), and Shanghai Engineering Research Center of Intelligent Vision and Imaging. Also, we extend our heartfelt gratitude to the anonymous users who participated in our user study.
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In this section, we provide additional discussions, details, and experiments to further support our contributions. The content is organized as
Section A - Discussions
Section B - Joint Noise Derivation
Section C - Implementation Details
- Section C.1 - Serial Prompting
- Section C.2 - Test Set for Quantitative Results
Section C.3 - Details of User Study
- Section C.4 - Code Used and License
Section D - Additional Experiments
- Section D.1 - Qualitative Results (comparisons and more visualizations)
- Section D.2 - User Study Quantitative Comparisons
- Section D.3 - Analysis on Joint Noise Sampling
- Section D.4 - Extensions (long video story, personalization, making an image move)
Limitations and Future Work. We look forward to further research on this method. While our method offers the advantage of being training-free and not requiring extra training data, it highly depends on the large foundation models LLMs [7, 4, 26] and LDMs [33]. Consequently, it would inherit the limitations of those large pre-trained models. For example, LDMs often struggle with generating images containing detailed faces and limbs, specific text, multiple objects, interactions between objects, etc, therefore our method has the same weakness. Moreover, LDMs are often sensitive to seed selections of initial noises [36], so when the initial frame is of low quality, our method tends to result in relatively poor performance as well. Additionally, although our method demonstrates improved temporal consistency to other zero-shot methods, we found that it is still challenging to maintain high temporal coherency between frames in the zero-shot setting. However, leveraging video data proves to be an effective solution for acquiring temporal priors. Therefore, how to combine the strengths of zero-shot methods and trained methods is a promising direction for future research.
Societal Impacts. It should be acknowledged that there can be ethical impacts like other generative models. As we adopt ChatGPT [26] and Stable Diffusion v1.5 [33], our method may inherit the bias of those two models. Also, although the results of our method of direct text-to-video generation are still a step away from convincingly photo-realistic videos, the risk of abuse, for example, generating fake, harmful, or discriminating content, should be aware.
B Joint Noise Derivation
First, let us consider the distribution of unified noise. It is composed by initial noise  $\mathbf{x}_T^* \sim \mathcal{N}(\mathbf{0}, \mathbf{I}_n)$  for each frame and can be represented as  $\mathbf{x}_T^{1:f} = [\mathbf{x}_T^*, \dots, \mathbf{x}_T^*]^T$ . The noise of any two frames in  $\mathbf{x}_T^{1:f}$  have the same values, and therefore their covariance is
$$
\operatorname {c o v} \left(\mathbf {x} _ {T} ^ {*}, \mathbf {x} _ {T} ^ {*}\right) = \mathbf {D} \left(\mathbf {x} _ {T} ^ {*}\right) = \mathbf {I} _ {n}. \tag {10}
$$
Thus the unified noise follows the distribution as
$$
p \left(\mathbf {x} _ {T} ^ {1: f}\right) = \mathcal {N} \left(\mathbf {0}, \mathbf {J} _ {f} \otimes \mathbf {I} _ {n}\right), \tag {11}
$$
where  $\mathbf{J}_f$  represents the all-one matrix of size  $f\times f$  and  $\otimes$  denotes the Kronecker product. The specific form of  $\mathbf{J}_f\otimes \mathbf{I}_n$  is
$$
\mathbf {J} _ {f} \otimes \mathbf {I} _ {n} = \left[ \begin{array}{c c c c} \mathbf {I} _ {n} & \dots & \dots & \mathbf {I} _ {n} \\ \vdots & \ddots & & \vdots \\ \vdots & \ddots & \ddots & \vdots \\ \mathbf {I} _ {n} & \dots & \dots & \mathbf {I} _ {n} \end{array} \right] \Bigg \downarrow^ {\underset {\sim} {\int}} \overset {f \times \mathbf {I} _ {n}} {\times}
$$
Second, let us consider the distribution of individual noise, in which each frame is independently sampled. Therefore, the covariance between any two frames is 0, and the distribution still follows a standard normal distribution:
$$
p \left(\boldsymbol {\delta} _ {T} ^ {1: f}\right) = \mathcal {N} \left(\mathbf {0}, \mathbf {I} _ {n f}\right) \tag {13}
$$
According to Section 4.2, the mixed noise is defined as  $\tilde{\mathbf{x}}_T^{1:f} \coloneqq \cos\left(\frac{\pi}{2}\lambda\right)\mathbf{x}_T^{1:f} + \sin\left(\frac{\pi}{2}\lambda\right)\delta_T^{1:f}$ . Since  $\mathbf{x}_T^{1:f}$  and  $\delta_T^{1:f}$  are independently sampled, the sum of the two still follows a normal distribution, with a mean of  $\mathbf{0}$  and a variance of
$$
\begin{array}{l} \sin^ {2} (\frac {\pi}{2} \lambda) \mathbf {I} _ {n f} + \cos^ {2} (\frac {\pi}{2} \lambda) \mathbf {J} _ {f} \otimes \mathbf {I} _ {n}) = \sin^ {2} (\frac {\pi}{2} \lambda) \left[ \begin{array}{c c c c c} \mathbf {I} _ {n} & \mathbf {0} & \dots & \dots & \mathbf {0} \\ \mathbf {0} & \ddots & \ddots & \ddots & \vdots \\ \vdots & \ddots & \ddots & \ddots & \vdots \\ \mathbf {0} & \dots & \ddots & \ddots & \mathbf {0} \end{array} \right] + \cos^ {2} (\frac {\pi}{2} \lambda) \left[ \begin{array}{c c c c c} \mathbf {I} _ {n} & \dots & \mathbf {I} _ {n} \\ \vdots & \ddots & \ddots \\ \mathbf {I} _ {n} & \dots & \mathbf {I} _ {n} \end{array} \right] \\ = \left[ \begin{array}{c c c c} \mathbf {I} _ {n} & \cos^ {2} (\frac {\pi}{2} \lambda) \mathbf {I} _ {n} & \dots & \cos^ {2} (\frac {\pi}{2} \lambda) \mathbf {I} _ {n} \\ \cos^ {2} (\frac {\pi}{2} \lambda) \mathbf {I} _ {n} & \ddots & & \cos^ {2} (\frac {\pi}{2} \lambda) \mathbf {I} _ {n} \\ \vdots & & \ddots & \vdots \\ \cos^ {2} (\frac {\pi}{2} \lambda) \mathbf {I} _ {n} & \cos^ {2} (\frac {\pi}{2} \lambda) \mathbf {I} _ {n} & \dots & \mathbf {I} _ {n} \end{array} \right] \\ = \mathbf {I} _ {n f} + \cos^ {2} \left(\frac {\pi}{2} \lambda\right) \left(\left(\mathbf {J} _ {f} - \mathbf {I} _ {f}\right) \otimes \mathbf {I} _ {n}\right) \tag {14} \\ \end{array}
$$
Thus, variable  $\tilde{\mathbf{x}}_T^{1:f}$  follows the following distribution.