EVA: An Embodied World Model for Future Video Anticipation

## Abstract

Abstract World models integrate raw data from various modalities—such as images and language to simulate comprehensive interactions in the world, thereby displaying crucial roles in fields like mixed reality and robotics.
Yet, applying the world model for accurate video prediction is quite challenging due to the complex and dynamic intentions of the various scenes in practice.
In this paper, inspired by the human rethinking process, we decompose the complex video prediction into four meta-tasks that enable the world model to handle this issue in a more fine-grained manner.
Alongside these tasks, we introduce a new benchmark named Embodied Video Anticipation Benchmark (EVA-Bench) to provide a well-rounded evaluation.
EVA-Bench focused on evaluating the video prediction ability of human and robot actions, presenting significant challenges for both the language model and the generation model.
Targeting embodied video prediction, we propose the Embodied Video Anticipator (EVA), a unified framework aiming at video understanding and generation.
EVA integrates a video generation model with a visual language model, effectively combining reasoning capabilities with high-quality generation.
Moreover, to enhance the generalization of our framework, we tailor-designed a multi-stage pretraining paradigm that adaptatively ensembles LoRA to produce high-fidelity results.
Extensive experiments on EVA-Bench highlight the potential of EVA to significantly improve performance in embodied scenes, paving the way for large-scale pre-trained models in real-world prediction tasks.

## 1 Introduction

A world model integrates raw data from various modalities such as images and language, to imagine how the world evolves as an agent behaves (Ha & Schmidhuber, ). It aims to understand the physical world they encounter and generate high-fidelity videos, allowing embodied agents to plan, evaluate, and simulate operations in their own neural space. Such a model shows great potential, particularly in fields like mixed reality, autonomous driving (Gao et al., ; Wang et al., ), gaming (Bruce et al., ), and robotics (Yang et al., ).

Imaging a world model with future prediction ability. It would not only understand environments but also interact by anticipating future actions and generating predictive videos. Such predictive videos could serve as interactive guidelines, akin to a product manual in mixed reality, a driving instructor, or a robot’s planning imagination (Du et al., ), greatly enhancing the decision-making process and helping the embodied agents towards Artificial General Intelligence.

This paper aims to enable embodied agents to generate predictive videos using multimodal instructions, allowing them to visualize potential outcomes before taking action.

However, existing methods (Zhou et al., ) usually focus on conditional simulation and overlook the complexities of multimodal and multi-level time-scale predictions.
Moreover, benchmarking such prediction tasks involves several critical steps, data collection, model training, validation, and continuous refinement, which remain largely unexplored.
To briefly, there are two main challenges in this task:

Figure: Figure 2: Giving the instruction and observation as input, the world model planning can be decomposed into several meta-questions. With the combination of these meta-questions, Rethinking, the world model gives a proper output.
Refer to caption: x2.png

- 1)
Lack of Benchmark: Video generation involves producing frame-level actions based on previous motion cues, guided by high-level language instructions (Xing et al., ). However, benchmarking these multilevel and multimodal predictions poses challenges due to the complexity of achieving a unified understanding of coarse-grained high-level language instructions.
- 2)
Training Models: Recent pre-trained general models usually struggle with significant inaccuracies and illusion outputs in embodied scenes (Xing et al., ). In contrast, the task-specific models exhibit limited generalization capabilities (Yan et al., ; Zhou et al., ). A major challenge lies in generating high-fidelity action predictions while preserving a comprehensive understanding of the world within a unified framework, particularly when dealing with complex instructions.

To benchmark this task, we start by reformulating the problem in a coarse-to-fine manner.
Given common sense that frame-level prediction of video generation is a minimum step of prediction, it is not enough for a task-level prediction. As shown in Figure 2, each task-level prediction step requires a comprehensive understanding of the current situation, followed by a next-step prediction of what will happen. Such prediction, in the human mind, could further become pictures or so-called imagination. Then we wonder whether this imagination satisfied this task or not. Such a rethinking process keeps going until this prediction step is completed. Therefore, with the inspiration of recent work (Wei et al., ; Liu et al., ; Kawaharazuka et al., ), we address these challenges by defining the task within embodied scenes into four meta tasks, Action-Description, How-To, Finish-Thinking, Next-Step, as shown in Figure 1. Furthermore, to evaluate such cases, we introduce an embodied video anticipation benchmark (EVA-Bench) to evaluate the video prediction task.

We further propose a novel world model, the Embodied Video Anticipator (EVA), which is capable of generating future prediction videos for embodied scenes, thereby enhancing the interaction experience between humans and machines. To achieve this, we re-collect published datasets from multiple domains to create a diverse dataset and introduce a multi-stage pretraining method. We build an embodied video predictor through a video generation model(VDM) and visual language model(VLM). Additionally, we implement a cross-attention alignment model for end-to-end optimization. We introduce a few-shot adaptation method with an Ensemble of LoRA Hu et al. () to ensure the model can adapt to specific cases efficiently and incrementally.

In summary, our contributions are as follows:

- •
Human-Rethinking-Inspired Rethinking Generation: The paper formulates the problem of video prediction by defining it within embodied scenes into four meta tasks: Action-Description, How-To, Finish-Thinking, and Next-Step. This structured approach helps in addressing the complexities of multi-level and multimodal predictions.
- •
Embodied Video Anticipator: We formulate the Embodied Video Anticipator (EVA) framework, a multimodal understanding and generation model for embodied scene generation. We integrate multi-stage pretraining, cross-attention alignment, and few-shot adaptation with an Ensamble-LoRA. Our model can give consistent and long-horizon video predictions.
- •
Embodied Video Prediction Benchmark Creation: We introduce EVA-Bench, a comprehensive benchmark designed to evaluate the performance of world models in predicting future events within embodied scenes. This benchmark provides a standardized way to measure and compare different models, addressing the challenge of defining and evaluating video anticipation.

## 2 Related Work

Video Generation With the advent of diffusion-based visual generation models, there has been significant progress in extending the capabilities of video generation. For instance, models like VideoCrafter (Chen et al., ; ) and VideoPoet (Kondratyuk et al., ) have demonstrated impressive abilities in generating high-quality video. Moreover, video generation models with image conditions like Dynamicrafter (Xing et al., ), Stable Video Diffusion (Blattmann et al., ) and Animatediff (Guo et al., ) meet impressive generation quality and have already been used in many areas. Such weakness also happens in some long video generation methods (Wang et al., ; Yin et al., ). These video-generation models lack reasoning abilities and still struggle with consistency and understanding, so recent works also combine generation models with LLMs (He et al., ).

World Model World models aim to provide future predictions based on current observations. This concept has been explored in various domains, including Genie (Bruce et al., ), which shows interesting ability in gaming simulation, Vista (Gao et al., ; Wang et al., ), etc., in autonomous driving. Video prediction is a special world-model-like task, Seer (Gu et al., ), AID (Xing et al., ) adapting image-to-video generation model to predict the motion of future frames. Additionally, world models such as RoboDreamer (Zhou et al., ) and AVDC (Ko et al., ) have been utilized as robot simulators. Unisim (Yang et al., ), for instance, combining pre-trained web-scale data with embodied videos expands world models’ applications. VLP (Du et al., ) integrated language and video generation models for robot planning but stayed at the concept level. A task-level video predictor is still needed.

Embodied Dataset and Benchmark On the other hand, such multimodal benchmarks are still missing. Open-X (Padalkar et al., ) provides comprehensive robot data, Ego-exo4d (Grauman et al., ) provides multi-level language annotation to key steps, and Webvid-10M-Action (Xing et al., ) updates the action annotation to the internet video dataset. The MMWorld (He et al., ) benchmark includes embodied tasks for visual-question-answer tasks. However, there is still a lack of a video-language-to-video-language dataset that can define and benchmark the prediction ability of the world model.

In summary, world models, whether as simulators or future predictors, require a well-defined task benchmark and model paradigm to advance the field.

Figure: Figure 3: self-ask (Press et al., ) inference pipeline of EVA. Given the visual observation and human questions as input, EVA would first generate fixed frames of videos and related text answers. Then, the model prompts itself to check the task completion status; if the predicted video is not finished, EVA keeps generating the extended frames until the task completion judgment is true.
Refer to caption: x3.png

## 3 Task Formulation and Benchmark

To benchmark the video prediction task, we first format it. In this section, we will first give a comprehensive task formulation explanation in Section 3.1, and then we will describe how we turn it into four meta-generation problems and how we benchmark them in Section 3.2.

### 3.1 Task Formulation

To begin with, we noticed that there is an existing multi-level of language prediction (Cheng et al., ); in this paper, we define frame-level, task-level, high-level, etc. For a simple example, task-level prediction is direct language instruction, like ”pick up the book”, and the serial task-level formulate a higher level instruction, e.g., ”Cleaning the table.” Modern video generation models can make frame-level predictions by extending the previous frames.

This paper aims to solve the video prediction problem at the task level. Given common sense that frame-level prediction of video generation is a minimum step of prediction, it is not enough for a task-level prediction. So, the formulation of a task-level prediction and a multi-round of frame-level prediction are required.

In our model, we define the world model function $\mathcal{M}$ as taking visual observations $O$ and a question $Q$ as inputs, producing a predicted video $\hat{V}$ and a text response $\hat{A}$ as outputs:

$$ $(\hat{V},\hat{A})=\mathcal{M}(O,Q)$ (1) $$

To ensure reliable task-level predictions, the model first comprehends the observed video $O$. It then decides whether to provide frame-level or task-level predictions. This decision is transformed into predicted videos $\hat{V}$, which are then iteratively refined until they meet task-level requirements. This process involves four key tasks: Action-Description (demonstrating understanding), How-To (indicating simulation quality), Finish-Thinking (checking task-level generation satisfaction), and Next-Step (showing task-level prediction capability). The world model can make further tasks or higher-level predictions only with a solid foundation in these four tasks.

### 3.2 Embodied Video Anticipator Benchmark

We establish a benchmark for task-level video prediction. Initially, we present a comprehensive design of evaluation metrics that evaluate the video-language to video-language generation task. We have named this the EVA-Score (EVAS). The representation of EVAS is as follows:

$$ $\textit{EVAS}_{\text{normalized}}^{t}=\frac{\sum_{i=1}^{n_{t}}\alpha_{t,i}\psi _{t,i}+\sum_{j=1}^{m_{t}}\beta_{t,i}\iota_{t,j}}{n_{t}+m_{t}}$ (2) $$

where $T=\{\text{Description},\text{Finish},\text{How-To},\text{Next-Step}\}$ represents the four tasks, $\psi_{t,i}$ is the score of the $i$-th language metric for task $t$, $\iota_{t,j}$ is the score of the $j$-th visual contentment metric for task $t$, and $\alpha$ and $\beta$ denote to the metric specific weight for better normalization. The indices $n_{t}$ and $m_{t}$ denote the number of language and visual metrics for each task $t$, respectively.

Therefore, we describe their language and video metrics for each task as follows:

Action-Description
We expect the text prompt to be short and simple, containing five key elements: subject, verb, object, location, and destination, following the basic English paradigm. We first evaluate this task using a series of text metrics to determine whether the output description closely matches the ground truth. We also added GPT4 (OpenAI, ) metric to judge the keywords and CLIP (Radford et al., ) score to evaluate the description quality.

Finsh-Thinking
This task aims to judge whether the frame-level prediction has completed the task. Real-world task generation has dynamic time durations based on different tasks. However, current video generation models can only generate a limited number of frames. In the VQA task, the finish-thinking output “Yes” or “No” could be used to calculate the accuracy. During generation, apart from the existing metric, we put forward a Goal Completion Estimation (GCE) metric to compare the last frame of ground truth and the generation frame.

How-To
For a “How-To” question, giving the instruction, the world model should be able to turn it into visual output. We introduce several video metrics to formulate the video evaluation metrics, including Motion Smoothness and Multi-stage Consistency inspired by VBench Huang et al. (). Combined with the text metrics, these form the correlation metric that represents the quality of “How-To” tasks. The final EVA-Score could benchmark the quality of How-To.

Next-Step
The last and most important task is to evaluate the task-level prediction of the next step in the video prediction system. We reorganized high-level labeling into task-level Next-Step QA problems to formulate the dataset. While evaluating, we compared the predicted action. We predicted video with the ground truth dataset and obtained the cross-modality correlation among these features, which is also included in our EVA-Score.

These questions help decompose the task into more specific and actionable components, enabling the model to better understand and predict future events across various scenarios. We anticipate that this benchmark and dataset will serve as a foundational setting for evaluating the capabilities of world models. We include the detailed dataset and benchmark information in the Appendix A.5.

## 4 Embodied Video Anticipator

Figure: Figure 4: A unified visual understanding and generation framework of EVA. The EVA introduces a visual projector in understanding LLM, an image conditional resampler in the generation model, trained a generation adapter as a text condition for denoising UNet, and added an Ensemble LoRA system for domain-specific generation. We train the EVA separately, including three stages of alignment and training.
Refer to caption: x4.png

To train the EVA, we designed a multimodal generative large language model, including two main pre-trained models for multimodal prediction. As we described in Section 3, we formulate the problem as a video-language to video-language task, which can be represented as equation 1. EVA uses a multistage training strategy, designed an Ensemble method for domain-specific LoRA Hu et al. () for VDM, interaction tokens to achieve this complex visual prediction task. We describe these key elements in detail in the following section.

### 4.1 VLM Backbone

VLM Backbone In EVA, we utilize a 7B Visual Language Model (VLM) backbone called ChatUniVi (Jin et al., ). The visual foundation model is CLIP (ViT-L/14) (Radford et al., ), while the LLM backbone is the Vicuna-v1.5 (Zheng et al., ) model. Moreover, an adaptive parameter-free token clustering algorithm  (Jin et al., ) is used to reduce the number of video tokens. This backbone setting introduces additional computational overhead and allows training to remain within the 2048-token limit. We first trained the visual project on the Video-Descrtption dataset. We then independently trained the full parameter fine-tuning of LLM on the QA Instruction Tuning dataset, as shown in Figure 4.

Interaction Token
Inspired by (Peng et al., ; Dong et al., ), we use special tokens to fit the task better. For image input, the VLM uses an <image> token as a placeholder within text tokens. Before being processed by LLM, the <image> token is replaced by visual feature tokens, obtained through a visual encoder and visual projector layer. For video inputs, the number of <image> tokens used corresponds directly to the number of frames in the video. During generation, we concatenate prefix token <IMG_P> with VLM language input as query embeddings to extract the feature. As shown in Figure 4, after obtaining the query embeddings, we use it as a condition for VDM to substitute for text prompt.

### 4.2 VDM Backbone

We utilize a 1.5B pre-trained Latent video diffusion model (VDM) called Dynamicrafter (Xing et al., ) for the generation model. The VDM conditions on image, fps, and language embedding features to generate a 16-frame video in 2 seconds. It UNet incorporates temporal and spatial transformers, as illustrated in Figure 4, and is pre-trained on a large-scale web video dataset, enabling it to generate dynamic content for open-domain images.

### 4.3 Ensemble of LoRA

We propose Ensemble-LoRA, a method designed to adapt to various domains by utilizing distinct LoRA modules. This approach ensures the highest generation quality in multiple robotic and egocentric environments while maintaining the generalization capabilities of the pre-trained Variational Diffusion Model.
Let $W$ represent the original weight matrices in the transformer layers. For each domain $d$, we train a low-rank adaptation:

$$ $\vspace{-0.5em}W_{d}=W+\Delta W_{d}=W+A_{d}B_{d}^{T}$ (3) $$

where $A_{d}$ and $B_{d}$ are low-rank matrices specific to domain $d$.
We applied LoRA after each transformer block in video diffusion to quickly adapt the video generation model to different tasks, as shown in Figure 4. Moreover, inspired by the Mixture of Experts (Jacobs et al., ), we proposed an Ensamble-LoRA for each domain by a Task Token controlled gating system (human-egocentric, real-robot, simulation-robot, etc.):

$$ $\vspace{-0.5em}g_{d}=\text{softmax}(f(\text{Task Token}))$ (4) $$

where $f$ is a function that maps the task token to gating values, and $g_{d}$ is the gating value for domain $d$. Therefore, the ensemble output for a given task is computed as:

$$ $\vspace{-0.5em}\hat{W}=W+\sum_{d}g_{d}\Delta W_{d}$ (5) $$

This formulation allows for efficient adaptation across different tasks without discarding previously learned adaptations.

### 4.4 Cross-Attention Generation Adapter

Our generation adapter employs a cross-attention module to align the VLM’s hidden features with the VDM’s text embedding features. Specifically, the adapter first applies a linear transformation to the VLM’s output to match the dimensionality of the VDM’s feature space. We use the diffusion denoise loss to train this adapter to achieve the best generation quality. The detailed module information is introduced in Appendix A.

### 4.5 Multi-stage Training Strategy

To train EVA, we employ a three-stage process. In the first stage, we train the Video-Language Model (VLM) and Video-Domain Model (VDM) separately, and using datasets like COCO (Chen et al., ) and a curated subset of CC3M-595K (Sharma et al., ) in VLM. The second stage involves aligning the VLM with an instruction tuning dataset, transforming it into an embodied QA bot using multimodal instruction data from sources such as MIMIC-IT (Li et al., ), LLaVA (Liu et al., ), and VideoChatGPT (Maaz et al., ), along with our EVA instruction tuning dataset (introduced in Section 5). In the final stage, we adapt the entire pipeline and train the adapter and VDM Ensemble-LoRA for each task, using the VLM’s output hidden embedding as a condition control signal. This stage addresses the diverse visual distribution among EVA-Instruction datasets(EVA-Instruct) by tuning the Ensemble-LoRA and adapter to maximize generation quality. More detailed information on these stages is provided in the appendix and experiment.

### 4.6 Self-Ask inference

Last, we propose self-ask inference steps to achieve the embodied prediction task. Given the question and visual observation, we first forward the inference of the VLM and obtain the hidden language state. The hidden state was later forwarded to the generation adapter, which was used as a condition to generate the prediction video. After each round of generation, the model follows the self-ask strategy (Press et al., ), which rethinks by asking the model itself, as shown in Figure 3, to check its prediction is a completed video. If the last frame does not satisfy the task completion requirement, the model will autonomously generate the extension of the video based on the previous prediction.

Figure: Figure 5: Visualization results of the How-To, Next-Step, and Finsh-Thinking. Starting from a random statue, EVA can generate robot motion and human-ego motion according to the instructions. The first two continuous cases show the long-horizon generation ability of EVA; in the last example, EVA can generate video based on its reasoning results. We include more example results on the demo page and in the Appendix B.
Refer to caption: x5.png

## 5 Experiments

In this section, we give a comprehensive experiment to evaluate the multimodal understanding and generation ability of EVA on four meta-tasks. First, we assess EVA’s VQA reasoning abilities on the Action-Description task in Section 5.1, highlighting the knowledge gaps in existing Vision-Language Models (VLMs) regarding embodied scenes. Next, we provide a detailed comparison of generation performance in Section 5.2, including the video score metrics from EVA-Score on the Finish-Thinking task. In Sections 5.3 and 5.4, we compare EVA against four baselines using EVA-Score to represent the capabilities when facing How-To and Next-Step tasks. Both qualitative and quantitative results are presented throughout, with additional qualitative analyses available in Appendix B. More video demonstrations can be found on the anonymous page at https://sites.google.com/view/eva-public.

EVA Instruction Tuning Dataset(EVA-Instruct) It encompasses four tasks featuring videos of humans, real-world robots, and simulated robots. The complete EVA instruction-tuning dataset consists of 500K QA pairs sourced from Open-X-Embodiment (Padalkar et al., ), Ego4d (Grauman et al., ), Ego-Exo4d (Grauman et al., ), and CALVIN (Mees et al., ). The EVA-Instruct is presented in a conversational format, paired with single images and videos as visual input. Data sources are summarized and more details are included in Appendix A.5.

Datasets for EVA-Bench. EVA-Bench includes a curated collection of 125 high-quality samples from our EVA-Instruct dataset. These samples encompass real-world robots, simulated robots, and egocentric human daily activities. The benchmark is categorized based on meta-tasks. details are included in Appendix C.

Implementation details. We set up two kinds of models, EVA-Generator and EVA. EVA-Generator uses Dynamicrafter as the backbone and fully fine-tunes it on the EVA-Instruct. EVA constructs an end-to-end pipeline with fine-tuned ChatUniVi as our VLM backbone and EVA-Generator as our VDM backbone, in the middle, we use a generation adapter to align the feature embedding among two backbones and only train the adapter with the EVA-Instruct.

### 5.1 Action-Description Video Question Answering

Metrics. First, we present the QA task results from our EVA-Bench. Comparing the BLEU (Papineni et al., ), METEOR (Banerjee & Lavie, ), ROUGE-L (Lin, ), CIDEr (Vedantam et al., ), SPICE (Anderson et al., ), and CLIP (Radford et al., ) scores as multimodal measures. Furthermore, to have a better word analysis, we use GPT-4o (OpenAI, ) as an automatic evaluator to obtain a GPT-4o score. The detailed metric description is included in Appendix A.5.

Main Results. In Table 1, Qwen2-VL-7B (Yang et al., ) and GPT-4o significantly outperform other models in zero-shot inference. However, under GPT-4o’s evaluation, while Qwen2-VL-7B remains the best-performing open-source model with a score of 29.58, the gap between the LLaVA-NeXT Liu et al. () series and Qwen2-VL-7B has noticeably narrowed.

**Table 1: Action-Description results in comparison of VLM. We compare the open-source VLM models. The QA prompts for each model are included in the appendix. In this table, blue means the best-untrained model.**
| Model | BLEU1$\uparrow$ | BLEU2$\uparrow$ | METEOR$\uparrow$ | Rouge-L$\uparrow$ | CIDEr$\uparrow$ | Spice$\uparrow$ | CLIP$\downarrow$ | GPT-4o$\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ChatUniVi-base (Jin et al., ) | 0.0969 | 0.0179 | 0.0640 | 0.1497 | 0.0427 | 0.0636 | 27.49 | 9.03 |
| LLAVA-Next-Interleaved (Li et al., ) | 0.0725 | 0.0152 | 0.0741 | 0.1174 | 0.0843 | 0.0982 | 29.63 | 26.94 |
| LLAVA-Next-Video (Zhang et al., ) | 0.0717 | 0.0249 | 0.0642 | 0.1062 | 0.1267 | 0.0961 | 30.36 | 25.56 |
| LLAVA-OneVision (Li et al., ) | 0.0874 | 0.0306 | 0.0591 | 0.1118 | 0.2172 | 0.1043 | 27.97 | 22.35 |
| Minicpmv2-6-7b (Hu et al., ) | 0.0672 | 0.0164 | 0.0572 | 0.0913 | 0.0404 | 0.0456 | 28.88 | 17.63 |
| qwen2-vl-7b (Yang et al., ) | 0.2484 | 0.1643 | 0.1434 | 0.3255 | 0.8914 | 0.2839 | 28.98 | 29.58 |
| GPT-4o (OpenAI, ) | 0.2651 | 0.1058 | 0.1671 | 0.2902 | 0.7355 | 0.3015 | 22.96 | 33.19 |
| ChatUniVi-loRA | 0.3007 | 0.1855 | 0.1054 | 0.3268 | 0.8245 | 0.2213 | 24.89 | 31.94 |
| ChatUniVi-Full-Parameter | 0.4105 | 0.1544 | 0.1809 | 0.4416 | 1.9012 | 0.3414 | 25.36 | 38.46 |
| EVA | 0.5735 | 0.5012 | 0.3095 | 0.5873 | 4.0139 | 0.5506 | 24.98 | 62.63 |

Compared to the zero-shot models, the ChatUniVi (fine-tuned on a 50K subset of our EVA instruction tuning dataset) has better performance across many metrics. Our EVA model remains the top performer, achieving an impressive score of 62.63 under GPT-4o’s evaluation. This group of comparisons showcases that the mixture of data we use in EVA outperforms fine-tuning on pre-trained weights, which supports our multi-stage training method.

### 5.2 Finish-Thinking Video Generation

Metrics We evaluate the task-condition generation quality of several models. We compare the video generation on Subject Consistency (SC), Background Consistency (BC), Motion Smoothness (MS), Dynamic Degree (DD) (Huang et al., ), Fréchet Video Distance (FVD), and especially Goal Completion Estimation(GCE) from task completion evaluation. The quantitative experiments are separated into three groups, as shown in Table 2. On the text domain, Finish-Thinking also includes VQA accuracy comparison on the output of Yes/No, which is not included in this table.

Main Results We fine-tune the full UNet of Dynamicrafter on the embodied video dataset named Dynamicrafter-Tune and compare it with the origin Dynamicrafter and EVA-Generator that use Ensamble-LoRA.
The EVA-Generator improves significantly in GCE and FVD, with scores of 86.83 and 177.28, respectively. These improvements are due to our effective LoRA design, which successfully adapts the base model to different scenes separately.

Then, we evaluate the video reconstruction ability of the understanding and generation union model. The input video is first converted into a text description by the VLM and then recreated using the first frame and the description. We compare different pairs of VLM+VDM with EVA.
Among these frameworks, EVA excels in SC(97.11), BC(96.01), and MS(99.31) and achieves the lowest FVD(184.81) score.
This experiment demonstrates the quality of EVA and highlights the efficiency of our multi-stage training and adaption strategy in preserving the generation quality.

**Table 2: Finish-Thinking Video Generation Quality Comparison. Subject Consistency(SC), Background Consistency(BC), Motion Smoothness(MS), dynamic degree(DD), aesthetic quality(AQ), Goal Completion Estimation(GCE), Fréchet Video Distance(FVD)**
| Model | Input | SC $\uparrow$ | BC $\uparrow$ | MS $\uparrow$ | DD $\uparrow$ | AQ $\uparrow$ | GCE $\uparrow$ | FVD$\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Dynamicrafter (Xing et al., ) | Image+Text | 87.25 | 91.91 | 96.72 | 63.33 | 43.57 | 80.96 | 362.56 |
| Dynamicrafter-Tune | Image+Text | 83.49 | 89.70 | 97.87 | 64.28 | 36.70 | 80.72 | 235.52 |
| EVA-Generator | Image+Text | 95.74 | 95.11 | 99.09 | 50.00 | 41.07 | 86.83 | 177.28 |
| ChatUniVi+Dynamicrafter | Video | 87.10 | 90.82 | 96.97 | 70.00 | 44.38 | 80.80 | 314.11 |
| qwen2-vl-7b+Dynamicrafter | Video | 87.13 | 91.39 | 96.29 | 73.33 | 44.19 | 81.96 | 307.33 |
| ChatUniVi+EVA-Generator | Video | 96.54 | 95.26 | 99.19 | 43.33 | 40.47 | 88.48 | 189.61 |
| qwen2-vl-7b+EVA-Generator | Video | 96.13 | 95.48 | 99.15 | 50.00 | 41.17 | 85.87 | 193,89 |
| LLAVA-OneVision+EVA-Generator | Video | 96.64 | 95.54 | 99.17 | 50.00 | 41.11 | 88.74 | 192.83 |
| EVA-2Stage | Video | 96.68 | 95.82 | 99.17 | 36.66 | 41.30 | 90.19 | 185.89 |
| EVA | Video | 97.11 | 96.01 | 99.31 | 46.67 | 41.72 | 89.09 | 184.81 |

**Table 3: How-To and Next-Step Task-Level generation evaluation result on the EVA-Bench.**
| Task | Model | EVAS-L $\uparrow$ | EVAS-V $\uparrow$ | EVA-Score $\uparrow$ |
| --- | --- | --- | --- | --- |
| HOW-TO | LLAVA-OneVision+EVA-Generator | 33.81 | 59.99 | 46.90 |
| qwen2-vl-7b+EVA-Generator | 41.54 | 60.40 | 50.97 |  |
| EVA-2Stage | 85.51 | 63.82 | 74.67 |  |
| EVA | 85.51 | 69.93 | 77.72 |  |
| Next-Step | LLAVA-OneVision+EVA-Generator | 16.75 | 51.75 | 34.25 |
| qwen2-vl-7b+EVA-Generator | 42.99 | 57.23 | 50.11 |  |
| EVA-2Stage | 73.02 | 62.10 | 67.56 |  |
| EVA | 73.02 | 65.34 | 69.18 |  |

Figure: Figure 6: Interaction example of “use the kettle.” For each step, input the new observation video, and EVA can generate the instruction video, teaching the user how to use the kettle as an MR handbook.
Refer to caption: x6.png

### 5.3 How-To Instruction Generation

In Table 3, which demonstrates significant improvements, EVA-2Stage achieved an EVAS-L score of 85.51, an EVAS-V score of 63.82, and an overall EVA-Score of 74.67. Our proposed model, EVA, outperformed all other approaches with an EVAS-V score of 69.93 and an EVA-Score of 77.72. These results highlight that the EVA model, with its multi-stage pretraining and cross-attention alignment, significantly enhances both predictive accuracy and generalization in the How-To task.

Furthermore, by comparing these results, we observed that aligning the output of VLM models to the language instruction format of embodied scenes is essential. Additionally, adopting an end-to-end approach further enhances the quality of video generation, demonstrating the flexibility and effectiveness of our proposed method. For the qualitative results, we demonstrate how EVA can drive an image with different prompts under real and simulation robots in Appendix B

### 5.4 Embodied Next-Step Anticaption

As shown in Table 3, for the quantitative result of the Next-Step task, EVA once again achieved the best performance. As a result, it provides a better text (semantic) condition to guide EVA-Generator for improved generation outcomes. In contrast, LLAVA-OneVision (Li et al., ) and Qwen2-VL-7B performed worse in this task compared to the How-To scenario due to their inability to accurately predict the next-step description. This clearly demonstrates the importance of a VLM that is thoroughly trained in embodied scenes for the Embodied World Model. Such deficiency in EVAS-Language also affects the generation quality, leading to the lower performance of LLAVA-OneVision(51.75) and Qwen2-VL-7B(57.23) on EVAS-Vision. This comparison also shows the overall consistency and quality of EVA-Score.

### 5.5 Qualitative analysis

In Figure 5, we show the visualization result of our meta tasks and the rethinking. In the first example, given the initial states of the robot and human instruction, we generate the motion “Move brown chip bag near white can.” However, such task planning can not be included in 16 frames. With a self-ask rethinking, EVA extends another 16 frames until the chips bag is near the white can. The third demo is an egocentric human QA. Given the previous video and Next-Step question, EVA gives the text response “Wipe hand” together with the extending video.

In Figure 6, we show the potential of using EVA as a handbook in mixture reality. Giving the observation frame and handbook instructions in green color, EVA could keep generating long-horizon videos that guide people on how to use the kettle.

We introduce extensive visualization results, including the action of the following ability in real and simulation robots, more robot Video QA results, and the large dynamic degree of EVA in egocentric human videos in Appendix A.5 and demo page.

## 6 Conclusion

In this paper, we introduced the Embodied Video Anticipator (EVA), a unified framework for video understanding and generation in embodied scenarios. By decomposing complex video prediction tasks into four meta-tasks and using the Embodied Video Anticipation Benchmark (EVA-Bench) for evaluation, we demonstrated significant improvements in predicting human and robot actions. EVA’s integration of a video generation model with a visual language model, along with a multi-stage pretraining paradigm, enhances its performance in real-world applications. Our extensive experiments highlight EVA’s potential to advance large-scale pre-trained models in practical prediction tasks.

Limitation
In this work, we benchmark the giving embodied video prediction task. However, such benchmarks only consider the interleaved video and language. In embodied scenes, we are expecting to introduce action, sensors, and more modalities into this dataset. Moreover, it will introduce more diverse scenarios, including autonomous driving, human-like robots, etc., in the future.

## Appendix A Appendix: Model Architecture and Training

EVA enables the pre-trained diffusion generator and visual language model to provide an autoregressive world prediction model.

### A.1 Vision Language Model

The Modern Video Language Model (VLM) is based on a Large Language Model (LLM). It leverages the powerful language capabilities of a pre-trained language model to transform the input image or video $I_{n}$ into latent visual features $\phi$ and then generates a language description output $\tau$. The representation equation of VLM is:

$$ $\tau=\textit{VLM}(\phi+\psi)$ (6) $$

where $\phi=\textit{Encoder}(I_{n})$, and $\psi$ represents the text embedding. Here, $\tau$ is typically in the language embedding sequences, and the visual encoder projects the visual content $I_{n}$ into the text embedding domain. By this method, VLM trains the visual information in an autoregressive format, similar to another language model.

Given our limited computational resources, it was essential to represent more video frames using fewer video tokens. ChatUniVi’s adaptive parameter-free token clustering method significantly reduces the number of video tokens without introducing additional computational overhead, allowing training to remain within the 2048-token limit. We independently trained the VLM on the Embodied-Video-Description dataset, as shown in Fig. 4.

We tested several VLM backbones, including ChatUniVi (Jin et al., ), LLaVA-OneVision (Li et al., ), LLaVA-NeXT (Liu et al., ), MiniCPM (Hu et al., ), and other mdoels. We found that while existing models were capable of generating detailed descriptions, they struggled with tasks involving prediction and planning, primarily due to the lack of relevant data in their training corpora. Additionally, the simplicity of the text prompts used in the diffusion model’s training data necessitated concise responses from the VLM. These responses needed to be composed of short, straightforward sentences that clearly include a subject, verb, object, location, and destination. As a result, we needed to fine-tune the VLMs fully on our dataset.

### A.2 Latent Diffusion Model

The Diffusion Model (Ho et al., ) is a type of generative model that iteratively refines a noisy input to generate high-quality data samples. It leverages a series of denoising steps to transform an initial noise distribution $\mathbf{z}_{0}\sim\mathcal{N}(0,\mathbf{I})$ into a desired data distribution $\mathbf{x}$. The process involves gradually removing noise from the input, guided by a learned model $\epsilon_{\theta}$ to produce a clear and coherent output. The latent diffusion model(LDM) further uses VAE to scale down the input features and reduce computation costs. Given the initial condition, the representation equation of the LDM  (Rombach et al., ) is:

$$ $\mathbf{x}_{T}=\textit{VAE}(\textit{Unet}(\mathbf{z}_{0})+\epsilon)$ (7) $$

where $\mathbf{z}_{0}$ is the initial noise, $\mathbf{x}_{T}$ is the final output, Unet is the denoising network, and $\epsilon$ represents the text or other control condition embeddings. The Variational Autoencoder (VAE) further decodes the latent feature into video.

In our comparisons with Animatediff (Guo et al., ), VideoCrafter2 (Chen et al., ), and Open-Sora (Zheng et al., ), we found that Dynamicrafter (Xing et al., ), which employs additional image condition injection methods, excels in retaining low-level features and maintaining high consistency for training-free longer video extensions. The core components of this VDM include a VAE encoder and decoder, an image condition resampler, and a denoising UNet.

### A.3 EVA Model Architecture

We employ a Vicuna-based VLM and a 3D U-Net architecture VDM to parameterize the EVA model. The model follows the ChatUniVi structure for VLM, and a standard 3D U-Net structure, with a spatial downsampling pass followed by an upsampling pass, utilizing skip connections from the downsampling activations. This process is interleaved with 3D convolution and attention layers. The model and training hyperparameters of EVA are summarized in Table 4 & 5.

**Table 4: Model Architecture for EVA.**
| Name | Type | Parameters |
| --- | --- | --- |
| VDM | UNet | 1.4B |
| VAE Encoder | AutoencoderKL | 83.7M |
| Image adapter | Resampler | 48.8M |
| Text adapter | Resampler | 32.3M |
| VLM | ChatUniVi | 7.0B |
| Query embedding | Linear | 262K |

**Table 5: Hyperparameters for training EVA diffusion model.**
| Hyperparameter | Value |
| --- | --- |
| Base channels | 320 |
| Optimizer | Adam ($\beta_{1}=0.9,\beta_{2}=0.999$) |
| Channel multipliers | 1, 2, 4, 4 |
| Learning rate | 0.0001 |
| Blocks per resolution | 2 |
| Batch size | 4 |
| Attention resolutions | 4, 2, 1 |
| Num attention heads | 64 |
| Conditioning embedding dimension | 4096 |
| Conditioning embedding MLP layers | 4 |
| Conditioning token length | 64 |
| Dropout | 0.1 |
| Training hardware | 8 Nvidia A800 chips |
| Training steps | 20000 |
| Diffusion noise schedule | cosine |
| Noise schedule log SNR range | [-20, 20] |
| Sampling timesteps | 50 |
| Sampling log-variance interpolation | $\gamma=0.1$ |
| Weight decay | 0.0 |
| Prediction target | $\epsilon$ |

### A.4 VLM Training Details

Stage 1: Video-Caption Alignment.
In the first stage, we train VLM and VDM separately to fit them into the embodied prediction domain. During VLM training, we aim to align the video encoder with a language model using image-caption pairs from various datasets, including COCO (Chen et al., ) and a curated subset of CC3M (Sharma et al., ) (CC3M-595K) screened by LLaVA (Liu et al., ).
The Visual Language Model (VLM) is pre-trained for one epoch with a batch size of 128, using the AdamW optimizer  (Kingma, ) and a cosine learning rate schedule. The learning rate is set to 2e-3, with a warmup rate of 0.03.

Stage 2: QA Instruction Tuning
In the second stage, we further align the VLM with the instruction tuning dataset, transforming it into an embodied QA bot. We incorporate open-domain multimodal instruction data from multiple sources: multimodal in-context instruction datasets (MIMIC-IT (Li et al., )), LLaVA visual instruction datasets (Liu et al., ), and video instruction data from VideoChatGPT (Maaz et al., ). We then add our EVA instruction tuning dataset as described in Section 3. All input images and frames are resized to 336 × 336. This stage is trained for two epochs with a batch size of 128 and a learning rate of 2e-5.

Stage 3: Adapting the Pipeline and Training Ensamble-LoRA
In the final stage, we adapt the entire pipeline and train the adapter and VDM Ensamble-LoRA specifically for each task, where with the inspiration of the mixture of experts (Jacobs et al., ) (Zhang et al., ) (Zhang et al., ).
This stage utilizes the EVA instruction tuning dataset for LoRA training. Despite the diverse visual distribution among EVA-Instruction datasets, the language annotations are similar.
Therefore, we propose a special tuning method that tunes the Ensamble-LoRA and adapter to maximize generation quality. In this setup, the generation adapter is trained on the whole dataset, while the Ensamble-LoRA is updated on each domain separately. We compare tuning VDM LoRA, CDM full-parameter tuning, and a two-stage inference method in Section 5.

### A.5 Training Dataset

To enhance comprehensive understanding, reasoning, planning, and video prediction capabilities in embodied environments, we
meticulously curate a comprehensive training dataset including 500K instances, termed EVA-Instruct. This dataset encompasses four tasks, each containing videos of humans, real-world robots, and simulation robots. To enhance the diversity of prompts, we employed ChatGPT-4v (OpenAI, )t to generate question-answer pairs, which were then applied to different tasks. The complete EVA instruction tuning dataset comprises 500K QA pairs collected from Open-X-Embodiment (Padalkar et al., ), Ego4d (Grauman et al., ), Ego-Exo4d (Grauman et al., ), and CALVIN (Mees et al., ). The data sources are shown as Table 6. The text in these datasets can be effectively restructured into components such as subject, verb, object, location, and destination, as illustrated in 7, making it highly suitable for our task requirements. The instructions for each meta-task are as follows.

**Table 6: Dataset name, number of training examples, and mixture weights used for EVA-Instruct.**
|  | Dataset | # Examples | Weight |
| --- | --- | --- | --- |
| Simulation | CALVIN  (Mees et al., ) | 23k | 0.85 |
| Real Robot | RoboVQA  (Sermanet et al., ) | 800k | 0.1 |
| RT-1 data  (Brohan et al., ) | 70k | 0.5 |  |
| Human activities | Ego4D  (Grauman et al., ) | 3.5M | 0.01 |
| Ego-Exo4D Keystep data  (Grauman et al., ) | 21k | 0.9 |  |

Instructions for action description. The list of instructions used to briefly describe the video content is shown in Table 8. They present the same meaning with natural language variance. Given the complexity of scene understanding in embodied environments, we aim to simplify the problem by selectively incorporating guidelines into the prompt with a probability of 50%. These guidelines are generated using GPT-4V shown in Table 9.

Instructions for How-to, Finish-Think, and Next-Step. The list of instructions used to construct the “How-to” format generation is shown in Table10. The instructions for the Finish-Think meta-task are shown in Table 11. Considering dataset differences, we constructed “next-step” prompts using the instructions from Table 12.

Data Construction for EVA Meta-Tasks. As shown in Table 6, the data sources for our four tasks are constructed using distinct instructions to create datasets for each meta-task. The datasets for the How-To and Action-Description tasks are relatively straightforward. Given that the textual annotations in embodied scene datasets generally follow the structure shown in Table 7, we only needed to extend the prompts using GPT-4o and standardize them into the format of subject, verb, object, location, and destination. Due to the ease of constructing the How-To and Action-Description tasks, we built two large datasets: How-To-200K and Action-Description-200K. For the Finish-Think dataset, our statistical analysis indicates that taking the first 25% of a video provides good examples of unfinished tasks. Additionally, some datasets within RoboVQA already contain questions regarding task completion, allowing for direct conversion. Based on this approach, we constructed the Finish-Think-50K dataset. Finally, for the next-step dataset, we utilized the key step annotations from the Ego-Exo4D dataset. This dataset marks key steps for each segment of a complete video, making it easier to convert into a next-step prediction task. In the Open-X-Embodiment’s real robot datasets, such as RoboVQA, which contain long-horizon task annotations involving a sequence of multiple steps, we converted these sequential steps into next-step tasks by focusing on the ordered steps provided. Using this approach, we constructed the Next-Step-50K dataset.

## Appendix B Appendix: Extensive experiments

In this section, we provided the results of the extent visualization experiment.
Figure 7 provides visualization results on simulation robot data, demonstrating that EVA could drive the robot by text instruction. The result shows that with proper action, the following is quite accurate.

Figure 8 and Figure 9 also show the action following generation ability of EVA in real robots. Figure 8 shows EVA could answer the question by generation the video.

Figure 10 is an ego-centric generation example. With proper training, the model has some planning abilities, generating a continuous motion sequence that first turns right and then gets the sugar.

Figure: Figure 7: We show the prompt following the ability of the EVA on the simulation robot. Given the same input video, the model can generate different actions according to different instructions.
Refer to caption: x7.png

Figure: Figure 8: EVA’s action control abilities on the real robot videos.
Refer to caption: x8.png

Figure: Figure 9: EVA’s action control abilities on the real robot videos.
Refer to caption: x9.png

Figure: Figure 10: EVA can do large Motion. In this example, the generation video first turns left and then gets the sugar with a different hand.
Refer to caption: x10.png

## Appendix C Appendix: EVA-Benchmark

To facilitate evaluation, the proposed EVA-Bench curated a collection of 125 high-quality samples from our EVA-Instrut, covering real-world robots, simulated robots, and egocentric human daily activities. These samples encompass diverse scenarios such as pick-and-place tasks, cooking, bike repair, COVID testing, and indoor organization. Drawing from existing datasets, we categorize them into three groups: egocentric human videos, real-world robots, and simulations. The statistical distribution of different scenes in our EVA-Bench is shown in Fig. 12.

### C.1 Benchmark Examples

We selected frames from the benchmark in three areas: egocentric human videos, real-world robots, and simulated robots. These frames showcase the richness and diversity of our embodied scenes, as shown in Fig. 11. The first three rows in the figure represent scenes from cooking, COVID testing, and bike repair. The fourth and fifth rows display indoor robotic arms manipulating various objects, while the sixth row shows scenes involving simulated robots.

Figure: Figure 11: Random frames from EVA-Bench.
Refer to caption: x11.png

Figure: Figure 12: This chart illustrates the distribution of various embodied scenes categories within the EVA-Bench.
Refer to caption: extracted/5940997/figure/bench_statics.png

### C.2 EVA Score Language Metrics

BLEU (Bilingual Evaluation Understudy Score):
BLEU1 and BLEU2 represent the BLEU scores using 1-gram and 2-gram matches, respectively. BLEU measures the overlap of n-grams between the generated text and reference text. Higher scores indicate greater similarity to the reference.

METEOR (Metric for Evaluation of Translation with Explicit ORdering):
METEOR considers factors like stemming, synonyms, and word order, making it more flexible than BLEU. It evaluates translation quality based on precision, recall, and a penalty for longer sentences. Higher scores indicate better translation quality.

ROUGE-L (Recall-Oriented Understudy for Gisting Evaluation - Longest Common Subsequence):
ROUGE-L measures the quality of text summaries and translations by calculating the longest common subsequence (LCS) between the generated and reference texts. It focuses on recall, with higher scores indicating better coverage of the reference content.

CIDEr (Consensus-based Image Description Evaluation):
CIDEr is used primarily for image description tasks. It evaluates the quality of descriptions by calculating the TF-IDF weighted n-gram similarity between the generated and reference descriptions. Higher scores indicate greater consistency with the reference descriptions. While calculating EVAS-Language points, we normalized the CIDEr by:

$$ $X_{\text{norm}}=X/10$ $$

where X is the CIDEr score.

SPICE (Semantic Propositional Image Caption Evaluation):
SPICE evaluates image descriptions by parsing the generated and reference descriptions into semantic graphs. It focuses on the semantic content and relationships within the descriptions. Higher scores indicate better semantic alignment with the reference descriptions.

CLIP(Contrastive Language-Image Pre-training) score:
CLIPScore is a reference-free evaluation metric for image captioning. Unlike traditional metrics that compare generated captions to reference captions, CLIPScore uses a pre-trained CLIP model to directly measure the similarity between the generated caption and the image itself. This approach leverages the model’s ability to understand both images and text, providing a robust evaluation of how well the caption describes the image. Higher CLIPScores indicate better alignment between the image and the generated caption.
In EVA Bench, we normalize the CLIP score between $0~{}1$ by:

$$ $X_{\text{norm}}=\frac{X^{\prime}-\min(X^{\prime})}{\max(X^{\prime})-\min(X^{ \prime})}$ $$

Where $x^{\prime}$ is the reciprocal of the $X$, we fix the upper and lower bound by summary the general testing result of different methods.

GPT-4o as a Judge: Unlike traditional similarity-based methods, GPT-4o emphasizes semantic understanding. In our implementation, we format the question, model output, and reference into a prompt, as outlined in Appendix C.4, and input it into the GPT-4o evaluator. The comparison between the generated and reference answers is based on four key criteria: object, action type, location, and attribute.
During the model evaluation, we observed that some generated responses, despite being semantically close to the ground truth, received low scores. Conversely, responses omitting key information occasionally received high scores. For example, the ground truth might state, “Cut out the tomato stem with a knife on the cutting board” while Qwen2-VL-7B (Wang et al., ) predicts, “Chop the tomato with a knife on the cutting board”. Despite high BLEU scores, the key difference between removing the stem and chopping the tomato remains significant.
Therefore, using GPT-4o as a judge to score QA text pairs and model-generated responses is essential. By leveraging GPT-4o’s advanced analytical and reasoning capabilities, we can more accurately evaluate the similarity between generated and reference texts. The specific prompt is detailed in Appendix C.4.

### C.3 EVA Score Video Metrics

Overall Consistency, Motion Smoothness, Background Consistency, and Subject Consistency metrics are inspired by the contributions from the open-source project VBench (Huang et al., ).

Overall Consistency: We also evaluate the overall consistency between video and text using ViCLIP (Wang et al., ), which measures how well the generated video aligns with general text prompts in terms of both semantics and style.

Motion Smoothness: While Subject Consistency and Background Consistency focus on the temporal consistency of the appearance, Motion Smoothness evaluates whether the motion in the generated video is smooth and adheres to real-world physical laws. This is assessed using motion priors from a video frame interpolation model.

Subject Consistency: This metric assesses the alignment between the subject in the input image and the subject in the generated video, also using DINO (Caron et al., ) features and order-statistics schemes.

Background Consistency: This metric evaluates the coherence between the background scene in the input image and the generated video. It utilizes DINO features and carefully designed order-statistics schemes.

Background Consistency: This metric evaluates the coherence between the background scene in the input image and the generated video. It utilizes DINO (Caron et al., ) features and carefully designed order-statistics schemes.

Aesthetic Quality: This metric refers to the perception of beauty or artistic value in a work, whether it’s traditional art, design, or even generative models. It encompasses elements like composition, color, texture, and form and how these elements evoke emotional responses from the viewer (Schuhmann et al., ).

Goal Completion Estimation: This metric estimates the final frame from the generative model compared to the ground truth. We evaluated the CLIP (Radford et al., ) feature of these two frames and normalized it. The greater the GCE, the generated image is to the target.

### C.4 Model Inference Prompts

Since the annotated answers in our dataset typically focus on key actions, composed of elements such as subject, verb, object, location, and destination, existing general VLMs struggle to generate responses in the same style. They often produce redundant or irrelevant scene descriptions. To address this, we designed specific prompts to guide the visual language models (VLMs) in generating concise, non-redundant answers. The prompt designs for various VLMs are listed in Table 13. For each meta-task in our EVA-Bench, all models, except for our EVA model, use the same prompts listed in Table 13.

### C.5 Evaluation Prompts

To evaluate general Vision-Language Models (VLMs) in a zero-shot setting, results using standard text evaluation metrics are often poor. Through experimental analysis, we found that traditional metrics like BLEU, METEOR, and ROUGE-L do not effectively capture similarities in actions, objects, locations, and other essential factors. Therefore, we follow the approach proposed by EgoThink (Cheng et al., ) and employ GPT-4O to assess the predictions of these VLMs.

However, directly using EgoThink’s prompts often leads to extreme scores, with many reasonable predictions being rated as 0. We believe this discrepancy stems from the increased complexity of tasks in embodied scenes, which demands a more nuanced and sophisticated evaluation framework.

For the step description generation task, we guide GPT-4o to assess predictions based on four key criteria: object, action type, location, and attribute. Each criterion is scored as follows: 1 if fully correct, 0.5 if partially correct or somewhat aligned, and 0 if incorrect. The final score is the average of these four criteria.

Object evaluates whether the objects involved in the action are correctly identified. Action type leverages GPT-4O’s reasoning ability to assess whether the predicted action aligns with the ground truth. For instance, comparing “get the fork from the table” and “pick up the fork from the table,” the use of “get” and “pick up” would result in a score of 0.5. Location assesses whether the location where the action is performed is accurately described, along with the broader context. If the action involves movement (e.g., moving an object from one place to another), the evaluation considers whether the starting point, destination, or path is correctly specified. Attribute examines whether the attributes of the objects involved (e.g., size, color, state, condition) are described accurately.

Our designed prompts are presented in Table 14.

Table: Table 7: The example answers of EVA-Instruct-Answer. Example answers from EVA-Instruct-Answer. In this context, C refers to the wearer of the egocentric camera, while x represents the other person involved in the interaction.

Table: Table 8: The list of instructions for action descriptions.

Table: Table 9: The list of instruction guidelines for action description.

Table: Table 10: List of instructions for How-to meta-task.

Table: Table 11: List of instructions for Finish-Think meta-task.

Table: Table 12: List of instructions for Next-Step meta-task.

**Table 13: Model inference prompts used for four meta-tasks.**
| Meta-Task | General Prompts |
| --- | --- |
| Action-Description | Question: {question}. Please provide a brief description in one sentence. The response should be clear and to the point, containing key action words such as objects, verbs, objects, location, and destination. Avoid unnecessary details or explanations. Please briefly describe the key action in a few words. |
| Finish-Think | This is a “ Finish-Think” task where you need to predict if a step is completed or not. Question: {question}. Please answer with either “ yes” or “ no”. |
| How-To | This is a “ How-to” task where you need to explain how to accomplish a specific task. Question: {question}. The response should be clear and to the point, containing key action words such as objects, verbs, objects, location, and destination. Avoid unnecessary details or explanations. Please briefly provide a simple step description in a few words. |
| Next-Step | This is a “ Next-Step” task where you need to predict the next step in a sequence of steps. Question: {question}. The response should be clear and to the point, containing key action words such as objects, verbs, objects, location, and destination. Avoid unnecessary details or explanations. Please briefly describe the next step in a few words. |

**Table 14: Model inference prompts used for four meta-tasks.**
| Model | Prompts for Evaluation |
| --- | --- |
| GPT-4O | [Instruction] You are tasked with evaluating the quality of the response provided by an AI assistant. The evaluation should focus on correctness, helpfulness, and relevance. Depending on the task type, you will evaluate specific attributes of a step-level generation tasks or score a simple yes/no question.<br>1. For step-level generation tasks, evaluate the assistant’s response based on the following attributes:<br>Object: Does the assistant mention the same or a closely aligned object as the reference? Minor but relevant differences (e.g., an additional unnecessary object) can receive partial credit, but introducing unrelated or missing key objects should lower the score.<br>Action Type: Is the action in the assistant’s answer precise and in line with the reference? If the intent of the action is similar but less precise, give partial credit. However, if the action significantly changes the task’s context or result, it should be more strictly penalized.<br>Location: Does the response correctly identify the location or context of the action? If the action involves movement (e.g., moving an object from one place to another), evaluate if the destination, starting point, or path are accurately described. Minor location discrepancies can receive partial credit, but if the location changes the context or goal of the action, assign a lower score.<br>Attribute: Are the attributes of the object(s) (such as size, color, state, or condition) correctly described? Missing or incorrect key attributes should lead to a lower score. If attributes are implied but still align with the context, partial credit can be given.<br>Scoring: If the reference answer does not include information for a particular attribute (e.g., object, action type, location, or attribute), do not score that attribute. For each attribute, assign:<br>- 1 if fully correct,<br>- 0.5 if somewhat correct or partially aligned,<br>- 0 if incorrect.<br>After evaluating each attribute, sum the scores and calculate the overall rating by averaging the individual scores. Do not round the final result. The final rating will be a non-rounded average score between 0 and 1.<br>2. For yes/no questions, directly evaluate whether the assistant’s response is correct: Assign 1 if the answer is correct, and 0 if it is incorrect.<br>After providing your analysis, rate the response with the calculated average score, formatted as: “Rating: [[average_score]]”. Now proceed with the evaluation based on the provided task:<br>[Task Type] {task_type},<br>[Question] {question},<br>[The Start of Reference Answer] {refanswer},<br>[The End of Reference Answer],<br>[The Start of Assistant’s Answer] {answer},<br>[The End of Assistant’s Answer]. |

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 2
questions: Is there any ordering bias in the next-step subtask? Since there are many possibilities for the next step, does the method develop any bias of the dataset when predicting the next step?
soundness: 3
strengths: 1. The paper shows an exhaustive evaluation on multiple metrics.
2. The contribution of designing the video understanding as a world model is interesting.
summary: The paper introduces a framework for video generation and video understanding in embodied scenarios through a world model approach. Inspired by the human thinking, the task of video understanding is broken down into four sub tasks - action description, finish thinking, how-to, and next-step along with video generation. This is introduced as a new benchmark, EVA-Bench. 

The world model function takes visual observations and a question as input to produce a predicted video and a text response. The action description consists of five key elements (subject, verb, object, location, destination), finish thinking does a frame level prediction to check if the task is completed, how-to task turns the instruction into a visual output, and next-step compares the predicted action video with ground truth video. The metrics of these sub tasks is named as EVA-Score which considers a weighted sum of language metric and visual contentment metric. 

The EVA framework is trained as a multi-stage pretraining network with cross-attention alignment and few shot adaptation with LoRA. The method is evaluated the four tasks on multiple metrics and shows an improvement when compared with the baselines. Overall, the contribution of a world model and dividing the video understanding into four subtasks following human thinking is interesting.
weaknesses: 1. The subtasks of how-to and next-step have a possibility of various scenarios. How-to task turns the instruction into a visual output and there can be multiple outputs to perform the same instruction. Similarly, in the next-step task, there can be multiple actions that are possible given the actions that have happened until now. How does the model take into account this possibility of various scenarios during training and in metric evaluation?

2. The task of finish thinking which decides if the task has finished on the basis of frame-level prediction might not be accurate in various scenarios. For example, in cooking scenarios, the end frame is not unique and can take up many variations of the same recipe. How does the current approach take into account the different variations of the generated frame?

3. Since the tasks are sequential, error compounding can also occur over time.

---

confidence: 4
contribution: 3
initial_rating: 5
presentation: 3
questions: - More convincing evaluations and analyses of the proposed embodied world model, examining how the four meta-tasks help the robotic tasks in addition to their benchmarks in EVA. It would be helpful to clarify the contributions of this paper.
- Please also point out the novelty of the proposed embodied video anticipator, in contrast to the recent state-of-the-art methods.
soundness: 3
strengths: **Clear Writing and Presentation**

- The paper is well-written and generally easy to follow. The problem it aims to address is indeed a challenge in the field of video generation and the embodied world model. It provides appropriate intuition and effectively builds up motivation where needed, as illustrated in Figures 1 and 2.

**Interesting Decomposition of the Task**

- The author successfully decomposes the task into more specific and actionable components. By employing a self-ask approach, a multi-task model is serially executed at the inference stage to achieve the final goal, which is a simple yet efficient method, as shown in Figure 3.

**Strong Results in the Designed Benchmark**

- EVA demonstrates strong performance compared to existing baselines in the designed EVA benchmark, which encompasses four meta-tasks with their task-specified metrics.
summary: This work aims to address the video prediction problem at the task level, i.e., generating a long video until task completion based on current observations and instruction, which is more challenging than traditional frame-level video prediction. The authors reformulate the task in a coarse-to-fine manner, similar to the approach in "Human Rethinking." Specifically, they decompose the generation of long temporal videos into multiple tasks, including Action-Description, How-To, Finish-Thinking, and Next-Step, thereby generating multiple short videos that are ultimately combined into a long video. Subsequently, based on these four meta-tasks, the authors collected corresponding datasets and obtained the EVA model by adopting multi-stage and efficient fine-tuning methods. Experiments across various dimensions validate the effectiveness of the proposed method.
weaknesses: **Novelty**

- The model's architecture is very similar to [1][2], as both utilize a Vision-Language Model (VLM) paired with a Video Diffusion Model (VDM). Currently, I have not observed any comparison or discussion between them in the paper (maybe the multi-stage pretraining scheme inspired by human rethinking?). Moreover, multi-stage pretraining and cross-attention alignment have become common training techniques in this field. Furthermore, I am unclear about the differences or advantages of the proposed Ensemble-LoRA compared to other existing LoRA-MOE technologies, like [3]; the authors also lack discussion or comparison with these methods in their experiments.

**Limitations in Evaluation Dimensions**

- Although the authors are tackling a very challenging problem, i.e., task-level video generation over long temporal sequences, all quantitative experiments focus on evaluating the four meta-tasks, without conducting quantitative evaluations on the complete videos. Additionally, since this is an embodied world model, the generated robot manipulation videos can be tested in simulation [4][5] or in real world [5]. For example, RoboDreamer [6], which the authors frequently mention, has performed quantitative evaluations on RLBench. In summary, the experimental results predominantly test short videos and lack comprehensive quantitative evaluations from various perspectives on the overarching problem, i.e., long video generation, they aim to solve.

**Lack of Ablation Studies**

- While the authors have proposed numerous methods (such as decomposing the overall task into four meta-tasks and employing various training strategies for fine-tuning), they have not conducted any ablation studies to verify the necessity of these methods or their specific roles within the framework. For instance, there is no discussion on which dataset/meta-task among the four meta-tasks is more critical for the model's performance, or how the omission of the Action-Description data affects the overall model. Again, if the proposed video generation can be applied to synthesized/real robotic manipulation tasks, it would be easier to verify the performance of this method, as well as how each meta-task helps.

[1] Hu A, et al. Gaia-1: A generative world model for autonomous driving

[2] Xiang, et al. Pandora: Towards General World Model with Natural Language Actions and Video States

[3] Chen Z, et al. Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE

[4] Du Y, et al. Learning universal policies via text-guided video generation

[5] Ko P C, et al. Learning to act from actionless videos through dense correspondences

[6] Zhou S, et al. RoboDreamer: Learning Compositional World Models for Robot Imagination

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 4
questions: All of my questions are asked in the weakness part

Typo: line 305 Ensamble---Ensemble

Figure 4 Resempler---Resampler?
soundness: 4
strengths: 1.  Well motivated: Two main challenges, lack of benchmark and failure in the in embodied scenes , are identified.  As a result, 4 meta tasks are unified into the a single framework to deal with multi-level prediction. 

2.  Readability; Well written. Easy to follow. Nice figure.

3.  Originality: A novel world model, the Embodied Video Anticipator (EVA), is designed for video prediction and understanding simultaneously. 

4.  New benchmark: Datasets from various domains are aggregated to build a diverse dataset and introduce a multi-stage pretraining approach.

5.  Good performance: EVA outperform all baselines on various tasks and benchmarks.
summary: This work proposed a a unified framework called Embodied Video Anticipator (EVA) for both video understanding and generation. A  video generation model was embedded into a VLM model. Comprehensive testing on EVA-Bench demonstrates EVA’s capability to greatly enhance performance within embodied environments, signaling a strong foundation for deploying large-scale pre-trained models in real-world predictive applications.
weaknesses: 1.  Ensemble LoRA: Why is the ensemble LoRA injected into the spatial part? If it’s responsible for the task-specific domain, wouldn’t it make more sense to inject it into the temporal transformer?  Since there is already an image encoder, a more precise explanation of the role of the ensemble LoRA would be helpful.


2.  A further analysis of the output of the gating function would be more helpful to demonstrate the effectiveness of the gating mechanism and to prove efficient adaptation.


3.  Embodied Video Prediction: Embodied Video Prediction involves not only first-person perspective videos but, more importantly, egocentric motion, which this work does not seem to explore. If there are examples related to generated videos or tasks in this area, it would be great to have them updated on the website.

4.  Ablation study:  It would be more helpful to see more ablation studies to verify the effectiveness of each newly added module or method.

---

confidence: 5
contribution: 3
initial_rating: 5
presentation: 3
questions: Please see weaknesses, especially 2,3 and 4.
soundness: 2
strengths: 1. The generation performance in both language and video level is significantly better than the compared methods.
2. The introduced EVA evaluates four interesting aspects of captioning, finish-thinking, how-to, and next-step.
summary: This paper introduces a unified framework for video understanding and generation in embodied scenarios called EVA. The model can take as input an image with an instruction, and autoregressively generate future images until the goal is estimated to be completed. This paper introduces a new benchmark EVA-Bench for evaluation, evaluating video generation quality, the finish-thinking estimation accuracy and action description quality. Language evaluation metrics and image generation evaluation metrics are both used for evaluation.
weaknesses: 1. The proposed method largely follows Pandora (https://arxiv.org/pdf/2406.09455), the most critical modification is its application on embodied data, and the reform of tasks. From a benchmark side, I believe this is interesting, but from a model side, this work seems incremental.
2. For the results in Table 1, I hope the authors can give more explanations on the comparison between EVA and "ChatUniVi-loRA", since these methods are very similar on the action description task. There is a big performance gap between ChatUniVi and EVA, however there is no explanation on this.
3. From Table 3, it is clear that the language part has a huge influence on the final score. It is understandable that LLaVa OneVision and qwen2-vl are not trained using the same data. But why the EVAS-V score is not largely influenced by EVAS-L score? Does it imply that one of these scores is not consistent with human understanding?
4. As for the EVA metrics, the video generation metrics largely follows the metrics in Vbench. However, I do not believe all these metrics should be used in the embodied scenario. For example, the aesthetic quality metric. For the Goal Completion Estimation metric, I believe this metric can be better designed. Now it is only the CLIP feature similarity. However I think this similarity is too semantic level and coarse-grained in the embodied AI scenario. This results in the GCE is almost similar for all models. I think spatial-awareness is critical in the GCE metric in the embodied AI scenario, and CLIP feature does not consider this. In the same embodiment with similar object presence, the CLIP features tend to be similar. I believe at least DINO features should be used.

**Post discussion comments.** 
Many thanks to the authors' responses in the discussion phase. After the discussion, my concerns for this work remain. The authors admit the lacks on the model side but fail to downweight it in the revision. This is not good, especially for a  paper with an emphasis on the model in its title. As for the benchmark, there remain concerns on the evaluation metrics. Using misleading metrics for a new benchmark is not the responsible way to contribute to the research community. Since the contributions in this work is also clear, I hope the authors can carefully re-draft and meticulously re-design the benchmark metrics, to make this work in a mature form to appear in a top-tier conference.

Meta-review: This paper proposes a world-model based approach for video-understanding and -generation in embodied scenarios. To this end, the authors introduce both a new benchmark and a model to address this task. Reviewers largely appreciated the overall direction and motivation for this paper.

However, after a lengthy discussion with Reviewer ECZA, some key points remain unaddressed: On the model side, the approach is incremental, and instead of emphasising the model in the paper, it may be more appropriate to highlight the benchmark instead. However, the paper was not revised to reflect this. Moreover, although the benchmark is promising, some concerns remain over its evaluation metrics. It is important for the reserach community to iterate on well-designed benchmarks where evaluation metrics correlate well with actual capabilities of models that users care about. Therefore, the AC agrees with Reviewer ECZA that the paper could do with another round of iteration and submission.

Decision: Reject